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

Feature Selection

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

Hiring signal: Understanding of feature selection internals

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: Phase 2, Lessons 01-09, 08 (feature engineering) Time: ~75 minutes

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 It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.