Regularization is applying a penalty to increasing the magnitude of parameter values in order to reduce overfitting. When you train a model such as a logistic regression model, you are choosing parameters that give you the best fit to the data. This means minimizing the error between what the model predicts for your dependent variable given your data compared to what your dependent variable actually is.
In the post we’ll get to know the Cross-validation strategies as from the Sklearn module. We’ll show the methods of how to perform k-fold cross-validation. All the iPython notebook code is correct for Python 3.6.
In the post we will show how to generate model data and load standard datasets using the sklearn datasets module. We use sklearn.datasets in the Python 3.
In this post we’ll show how to make a linear regression model for a data set and perform a stochastic gradient descent in order to optimize the model parameters. As in a previous post we’ll calculate MSE (Mean squared error) and minimize it.
In this post we’ll share with you the vivid yet simple application of the Linear regression methods. We’ll be using the example of predicting a person’s height based on their weight. There you’ll see what kind of math is behind this. We will also introduce you to the basic Python libraries needed to work in the Data Analysis.