Model Evaluation
A model is only as good as the way you measure it.
Prerequisites: Phase 1 (Probability & Distributions, Statistics for ML), Phase 2 Lessons 1-8
#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
Keep going. The rest of this lesson is one step away.
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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.