Feature Engineering for Modern Machine Learning with Scikit-Learn

Chapter 6: Introduction to Feature Selection with Lasso and Ridge

Feature selection is a crucial technique in data science and machine learning that aims to identify the most relevant features contributing to model predictions. By reducing the number of features, this process enhances model interpretability, reduces computational load, potentially improves accuracy, and mitigates overfitting. In this chapter, we delve into two prominent regularization techniques: Lasso and Ridge regression.

These techniques serve multiple purposes in the realm of machine learning:

• Handling multicollinearity: They address the issue of highly correlated features, which can lead to unstable and unreliable coefficient estimates.
• Preventing overfitting: By adding penalties ...