Phase 3: Production ML Practices · ~75 minutes · Python

Feature Selection

More features is not better. The right features is better.

What you will learn

Implement filter methods (variance threshold, mutual information, chi-squared) and wrapper methods (RFE, forward selection) from scratch
Explain why mutual information captures nonlinear feature-target relationships that correlation misses
Compare L1 regularization (embedded selection) with RFE (wrapper selection) and evaluate their computational tradeoffs
Build a feature selection pipeline that combines multiple methods and demonstrate improved generalization on held-out data

Introduction

Type: Build
Language: Python
Prerequisites: Feature Engineering & Selection
Time: ~75 minutes

Objective

Learning objectives

Implement filter methods (variance threshold, mutual information, chi-squared) and wrapper methods (RFE, forward selection) from scratch
Explain why mutual information captures nonlinear feature-target relationships that correlation misses
Compare L1 regularization (embedded selection) with RFE (wrapper selection) and evaluate their computational tradeoffs
Build a feature selection pipeline that combines multiple methods and demonstrate improved generalization on held-out data

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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