By now you can build several kinds of model. This module is about building them well. You will learn why checking your test set repeatedly quietly corrupts it, and how cross-validation gives you an honest estimate without burning your final holdout. From there you will tune hyperparameters systematically rather than by trial and error, and finish with feature engineering — the encoding, scaling, and construction work that often improves a model more than switching algorithms ever would.
Learning Objectives
By the end of this module, you will:
- Explain how repeated test-set evaluation leads to contamination and optimistic estimates
- Implement k-fold cross-validation with
cross_val_score() and cross_validate()
- Compare models using cross-validation scores rather than test-set performance
- Explain the bias–variance tradeoff and identify underfitting versus overfitting
- Use
GridSearchCV to search hyperparameters systematically, and compare grid, random, and Bayesian search
- Encode categorical variables and scale numeric features appropriately
- Create polynomial, interaction, and domain-driven features, and handle missing data deliberately
- Assemble preprocessing and modeling into scikit-learn pipelines that prevent data leakage
Module Resources
Lab
| 🧪 Lab 12: Model Tuning |
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