How Overfitting Ruins Your Feature Selection

This post details how overfitting can ruin feature selection by causing inconsistent feature importance rankings, discarding relevant features, selecting irrelevant features, increasing sensitivity to data variability, and ultimately leading to poor generalization. It provides an example using a decision tree and discusses strategies to prevent overfitting in feature selection, such as using regularization techniques (L1, L2, Elastic Net) and model constraints.