Regularization with Linear Models

A huge part of machine learning (ML) is made up of linear models. Although sometimes considered less powerful than their nonlinear counterparts (such as tree-based models or deep learning models), linear models do address many concrete, valuable problems. Customer churn and advertising optimization are just a couple of problems where linear models may be the right solution.

In this chapter, we will cover the following recipes:

  • Training a linear regression with scikit-learn
  • Regularizing with ridge regression
  • Regularizing with lasso regression
  • Regularizing with elastic net regression
  • Training a logistic regression model
  • Regularizing a logistic regression model
  • Choosing the right regularization

By the end of ...

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