Phase 2: Ensembles & Evaluation · ~90 minutes · Python

Model Evaluation

A model is only as good as the way you measure it.

What you will learn

Implement K-fold and stratified K-fold cross-validation from scratch and explain why stratification matters for imbalanced data
Compute precision, recall, F1, AUC-ROC, and regression metrics (MSE, RMSE, MAE, R-squared) from scratch
Interpret learning curves to diagnose whether a model suffers from high bias or high variance
Identify common evaluation mistakes including data leakage, wrong metric selection, and test set contamination

Introduction

Type: Build
Languages: Python
Prerequisites: the Math for AI & ML course
Time: ~90 minutes

Objective

Learning objectives

Implement K-fold and stratified K-fold cross-validation from scratch and explain why stratification matters for imbalanced data
Compute precision, recall, F1, AUC-ROC, and regression metrics (MSE, RMSE, MAE, R-squared) from scratch
Interpret learning curves to diagnose whether a model suffers from high bias or high variance
Identify common evaluation mistakes including data leakage, wrong metric selection, and test set contamination

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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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