Model Validation & Hyperparameter Tuning

Trust, but verify.

Introduction

Validation ensures your model generalises to unseen data. Tuning optimises model performance systematically.

What You Will Learn

• Implement train-test splits and cross-validation correctly
• Apply grid search, random search, and Bayesian optimisation
• Interpret learning curves to diagnose model issues
• Report model performance with appropriate metrics and uncertainty

Assessment Connection

Section B (Results) — the rubric distinguishes "fail to evaluate" (40–49%) from "advanced validation with confidence intervals" (60–69%+).

Content

TutorialsHow-to GuidesReferenceExplanation

• Train-Test Splitting
• Cross-Validation Strategies
• Grid & Random Search
• Bayesian Optimisation with Optuna
• Learning Curves & Diagnostics

• Prevent Overfitting
• Cross-Validate Time Series
• Compare Models Statistically
• Report Confidence Intervals

• Sklearn Model Selection API
• Hyperparameter Cheatsheet by Algorithm
• Metrics Reference Table

• Overfitting vs Underfitting
• Why Cross-Validation Works
• Choosing the Right Metric