"
],
"text/plain": [
" temp atemp humidity windspeed casual registered month hour\n",
"0 9.84 14.395 81 0.0 3 13 1 0\n",
"1 9.02 13.635 80 0.0 8 32 1 1\n",
"2 9.02 13.635 80 0.0 5 27 1 2\n",
"3 9.84 14.395 75 0.0 3 10 1 3\n",
"4 9.84 14.395 75 0.0 0 1 1 4"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### We now train a model with SGDRegressor\n",
"SGDRegressor develops a model based on [Stochastic Gradient Descent](/linear-regression-stochastic-gradient-descent/).\n",
"\n",
"We supply the default paramets for the regressor model."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'alpha': 0.0001,\n",
" 'average': False,\n",
" 'early_stopping': False,\n",
" 'epsilon': 0.1,\n",
" 'eta0': 0.01,\n",
" 'fit_intercept': True,\n",
" 'l1_ratio': 0.15,\n",
" 'learning_rate': 'invscaling',\n",
" 'loss': 'squared_loss',\n",
" 'max_iter': 5,\n",
" 'n_iter_no_change': 5,\n",
" 'penalty': 'l2',\n",
" 'power_t': 0.25,\n",
" 'random_state': 0,\n",
" 'shuffle': True,\n",
" 'tol': 0.001,\n",
" 'validation_fraction': 0.1,\n",
" 'verbose': 0,\n",
" 'warm_start': False}"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regressor = linear_model.SGDRegressor(random_state = 0, max_iter=5)\n",
"regressor.get_params()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Train the model `regressor.fit()` \n",
"2. Estimate the model with MAE `mean_absolute_error`"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"data": {
"text/plain": [
"9340385490952.236"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regressor.fit(train_data, train_labels)\n",
"metrics.mean_absolute_error(test_labels, regressor.predict(test_data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number is too big...\n",
"\n",
"Let's output the targets and model predictions:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Target\n",
" [525 835 355 222 228 325 328 308 346 446]\n"
]
}
],
"source": [
"print('Target\\n', test_labels[:10])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediction\n",
" [1.47320172e+13 2.64787988e+13 1.23495857e+13 1.35429951e+13\n",
" 1.25998915e+13 1.74809610e+13 1.75786549e+13 2.11426913e+13\n",
" 1.96417926e+13 2.32672606e+13]\n"
]
}
],
"source": [
"print('Prediction\\n', regressor.predict(test_data)[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model predicts super big numbers. It should not be this way.\n",
"\n",
"Let's take a look at the regression coefficients."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.12866511e+10, 6.68434729e+10, -7.05920006e+10, -2.09051100e+10,\n",
" 1.65497847e+11, 2.92967602e+10, -6.71456293e+10, 8.32559786e+10])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regressor.coef_"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"The regression model coefficients are too high. This might happen if the features differ in scale. \n",
"### Scaling the features"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We separately apply **StandardScaler** to the train and test data."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"# create StandardScaler and train/fit it\n",
"scaler = StandardScaler()\n",
"scaler.fit(train_data, train_labels)\n",
"scaled_train_data = scaler.transform(train_data)\n",
"scaled_test_data = scaler.transform(test_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After we've got scaled data we train the model again."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"data": {
"text/plain": [
"0.11793762963431514"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regressor.fit(scaled_train_data, train_labels)\n",
"metrics.mean_absolute_error(test_labels, regressor.predict(scaled_test_data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The error is in this case is very low. \n",
"\n",
"If we output targets and predictions (below), we see that the model has done a good estimation. The distance is less than 1 bike."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[525 835 355 222 228 325 328 308 346 446]\n"
]
}
],
"source": [
"print(test_labels[:10])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[524.83, 834.83, 354.86, 221.89, 227.84, 324.88, 327.9, 307.93, 345.9, 445.9]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(map(lambda x : round(x, 2), regressor.predict(scaled_test_data)[:10]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The regression result is suspiciously good\n",
"We take a look at the coefficients and see that almost all of the weights are too small."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"regressor.coef_\n",
" [1.41, -1.41, 0.01, -0.04, 50.86, 148.0, -0.01, 0.01]\n"
]
}
],
"source": [
"#print(regressor.coef_)\n",
"map_obj = map(lambda x : round(x, 2), regressor.coef_)\n",
"print('regressor.coef_\\n', list(map_obj))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What are the features with non-small weights, 50.86, 148.0 ? \n",
"\n",
"*Casual* and *register* features are."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### How does the bike rental system work?\n",
"The bike rental system works as follows: both registered and unregistered users can use the system. In this case, column **registered** represent the number of registered users who use the system. The **casual** column shows the number of users who have not registered but also want to rent a bike. \n",
"\n",
"Let's output the values of the target function and see the following pattern: if the **value of these two columns (*registered* and *casual*) is added together, we get our target label**."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([16, 40, 32, 13, 1, 1, 2, 3, 8, 14], dtype=int64)"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_labels[:10]"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.all(train_data.registered + train_data.casual == train_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This was our mistake. We indeed used the features in dataset (**casual**, **registered**) that replicate the target, **count**. \n",
"\n",
"So we cut off these data (columns) from train and test sets to properly build regession model."
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "\"['casual' 'registered'] not found in axis\"",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'casual'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'registered'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'casual'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'registered'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m 4161\u001b[0m \u001b[0mweight\u001b[0m \u001b[1;36m1.0\u001b[0m \u001b[1;36m0.8\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4162\u001b[0m \"\"\"\n\u001b[1;32m-> 4163\u001b[1;33m return super().drop(\n\u001b[0m\u001b[0;32m 4164\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4165\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m 3885\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3886\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3887\u001b[1;33m \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3889\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m 3919\u001b[0m \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3920\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3921\u001b[1;33m \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3922\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3923\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m 5280\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5281\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5282\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5283\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5284\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: \"['casual' 'registered'] not found in axis\""
]
}
],
"source": [
"train_data.drop(['casual', 'registered'], axis = 1, inplace = True)\n",
"test_data.drop(['casual', 'registered'], axis = 1, inplace = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We scale the features with the new dataset and train/fit the model and estimate by MAE."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"scaler.fit(train_data, train_labels)\n",
"scaled_train_data = scaler.transform(train_data)\n",
"scaled_test_data = scaler.transform(test_data)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"data": {
"text/plain": [
"121.81123864993025"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regressor.fit(scaled_train_data, train_labels)\n",
"metrics.mean_absolute_error(test_labels, regressor.predict(scaled_test_data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above MAE is more realistic.\n",
"\n",
"The all model weights are influence the model predictions."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[31.03, 29.96, -41.93, 6.17, 14.08, 49.6]\n"
]
}
],
"source": [
"print(list(map(lambda x : round(x, 2), regressor.coef_)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pipeline\n",
"\n",
"Up to now we were solving the problem with default parameters of the regressor model. Now we have a certain base-level model.\n",
"\n",
"Now we will select optimal parameters for a regressor model.\n",
"\n",
"Yet to train/fit the model we need to scale [only] a train dataset. Since we use cross-validation (CV) we'll need to scale each CV training dataset at each CV step. Therefore we make a **Pipeline**. Pipeline implies transformation chains. Particulary it'll include 2 chains: (1) scaling and (2) regressotion itself at each iteration. *Sklearn* library does allow us to do both scaling of at each iteration and CV thru **Pipeline** module."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.pipeline import Pipeline"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"# make a pipeline from two steps: scaling and classification\n",
"pipeline = Pipeline(steps = [('scaling', scaler), ('regression', regressor)])\n",
"# both methods `scaler` and `regressor` should posess methods fit() and perdict()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"data": {
"text/plain": [
"121.81123864993025"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipeline.fit(train_data, train_labels)\n",
"metrics.mean_absolute_error(test_labels, pipeline.predict(test_data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The error (above) remained the same. Pipeline works well.\n",
"### Parameters selection\n",
"We select parameters by grid. \n",
"\n",
"In the case of using Pipeline, we need to refer to the model parameters using the extended name. \n",
"\n",
"First, we need to specify the name of the step, then a double underscore goes, then the name of the parameter itself follows."
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['memory', 'steps', 'verbose', 'scaling', 'regression', 'scaling__copy', 'scaling__with_mean', 'scaling__with_std', 'regression__alpha', 'regression__average', 'regression__early_stopping', 'regression__epsilon', 'regression__eta0', 'regression__fit_intercept', 'regression__l1_ratio', 'regression__learning_rate', 'regression__loss', 'regression__max_iter', 'regression__n_iter_no_change', 'regression__penalty', 'regression__power_t', 'regression__random_state', 'regression__shuffle', 'regression__tol', 'regression__validation_fraction', 'regression__verbose', 'regression__warm_start'])"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipeline.get_params().keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We compose a dictionary of parameters to choose from."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"parameters_grid = {\n",
" 'regression__loss' : ['huber', 'epsilon_insensitive', 'squared_loss', ],\n",
" 'regression__max_iter' : [3, 5, 10, 50], \n",
" 'regression__penalty' : ['l1', 'l2', 'none'],\n",
" 'regression__alpha' : [0.0001, 0.01],\n",
" 'scaling__with_mean' : [0., 0.5], #\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We build a CV grid and pass the **pipeline** object into it along with **parameters_grid** and others.\n",
"\n",
" - Parameter **scoring** defines the kind of error that we score parameters at."
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"grid_cv = model_selection.GridSearchCV(pipeline, parameters_grid, scoring = 'neg_mean_absolute_error', cv = 4)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 22.6 s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1208: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\"Maximum number of iteration reached before \"\n"
]
},
{
"data": {
"text/plain": [
"GridSearchCV(cv=4,\n",
" estimator=Pipeline(steps=[('scaling', StandardScaler()),\n",
" ('regression',\n",
" SGDRegressor(max_iter=5,\n",
" random_state=0))]),\n",
" param_grid={'regression__alpha': [0.0001, 0.01],\n",
" 'regression__loss': ['huber', 'epsilon_insensitive',\n",
" 'squared_loss'],\n",
" 'regression__max_iter': [3, 5, 10, 50],\n",
" 'regression__penalty': ['l1', 'l2', 'none'],\n",
" 'scaling__with_mean': [0.0, 0.5]},\n",
" scoring='neg_mean_absolute_error')"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"grid_cv.fit(train_data, train_labels)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-108.61772632999148\n",
"{'regression__alpha': 0.01, 'regression__loss': 'squared_loss', 'regression__max_iter': 3, 'regression__penalty': 'l2', 'scaling__with_mean': 0.0}\n"
]
}
],
"source": [
"print(grid_cv.best_score_)\n",
"print(grid_cv.best_params_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Results of the CV grid parameters search\n",
"The CV parameters grid shows good result - `neg_mean_absolute_error`: -108.62 \n",
"\n",
"We are ok with 3 iterations - `regression__max_iter`: 3 \n",
"\n",
"Regularization parameter - `regression__alpha`: 0.01\n",
"\n",
"Regularization type is L2, Ridge - `regression__penalty`: 'l2'\n",
"\n",
"As to the scaling, the best is with mean at the point 0 - `scaling__with_mean`: 0.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Score a model by the deferred test\n",
"Calcualte the MAE of *griv_CV* best estimator."
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"119.98978845935379"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics.mean_absolute_error(test_labels, grid_cv.best_estimator_.predict(test_data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This error is less than one of the base model.\n",
"\n",
"Yet, the MAE is at a certain point. What is the mean value of the test labels/targets?"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"232.159"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(test_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, even though we've decreased the error from 122 to 120, since the mean is 232, the model has not underwent a good improvment."
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
"test_predictions = grid_cv.best_estimator_.predict(test_data)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[525 835 355 222 228 325 328 308 346 446]\n"
]
}
],
"source": [
"print(test_labels[:10])"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[139.60470681 159.80765341 207.55935972 237.76288054 257.83836668\n",
" 267.44558034 272.49537469 297.70688522 304.29818873 313.58821156]\n"
]
}
],
"source": [
"print(test_predictions[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The target and predictions of grid_cv differ siginificantly. \n",
"\n",
"### Targets-predictions grapth\n",
"We display a graph of our objects in the space of the correct values of the target label vs our predictions. When we plot a graph in such a space a good model should have the following: a cloud of points in the diagonal area. It turns out that our predictions should coincide with the target label therefore the diagonal points should transpire."
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-100.0, 1100.0)"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
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