In this post we’ll show how to build regression linear models using the sklearn.linear.model module.
See also the post on classification linear models using the sklearn.linear.model module.
The 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.
See the practical example how to deal with overfitting by the regularization.
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.
In the post we share some basics of classification and clustering in Machine learning. We also review some of the cluster analysis methods and algorithms.