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 this chapter, we will have learned how to use and regularize some of the most commonly used linear models.
