Module 03: Model Evaluation

Evaluating machine learning models is as critical as training them. A model that performs well on training data but fails in production is useless. This module covers the essential techniques and metrics to rigorously assess model performance, diagnose issues like overfitting and underfitting, and select the best model for your problem.

Module Contents

1. Bias-Variance Tradeoff

Understand the fundamental tension in supervised learning. Learn how to decompose error into bias, variance, and noise, and how to diagnose underfitting and overfitting using learning curves.

2. Evaluation Metrics

Go beyond simple accuracy. Master classification metrics (Precision, Recall, F1-Score, ROC/AUC) and regression metrics (MSE, MAE, R-squared) to evaluate models in real-world scenarios, including imbalanced datasets.

3. Cross-Validation

Learn robust validation techniques to estimate model performance on unseen data. We cover K-Fold, Stratified K-Fold, Leave-One-Out (LOOCV), and time-series splitting strategies.

Module Review

Review key concepts, test your knowledge with interactive flashcards, and grab a quick reference cheat sheet for your next interview or project.