After generating potentially many features in the previous steps, the next task is to determine which ones are most useful for model building. Including irrelevant or redundant features can increase computational cost, make models harder to interpret, and potentially lead to overfitting. Feature selection aims to identify and retain only the most informative features from the original set.

This chapter introduces techniques to systematically reduce the number of features while preserving or even improving model performance. You will learn about:

• Filter methods: These techniques assess features based on their statistical properties, such as variance (using VarianceThreshold) or relationship with the target variable (using tests like ANOVA F-value or $χ^2$), without involving a specific machine learning model.
• Wrapper methods: These methods use a predictive model to score subsets of features. We will cover techniques like Recursive Feature Elimination (RFE), which iteratively removes the least contributing features.
• Embedded methods: These approaches incorporate feature selection directly into the model training process. Examples include L1 regularization (Lasso), which can shrink the coefficients of less important features to zero, and utilizing feature importance scores derived from tree-based algorithms like Random Forests.

By the end of this chapter, you will be equipped to apply various feature selection strategies using libraries like Scikit-learn to build more efficient and effective machine learning models.