Part III – Model Evaluation & Tuning¶
"A model is only as good as its ability to be measured and improved—evaluation and tuning turn potential into performance."
From Training to Trustworthy Models¶
You've learned the core algorithms and how they work. Now comes the critical phase where you transform trained models into reliable, high-performance systems.
But here's where many practitioners stumble: - Models that look great on paper fail in the real world - Default hyperparameters lead to suboptimal performance - Uncalibrated probabilities make decision-making unreliable - One-size-fits-all thresholds ignore business costs
Part III bridges the gap between model training and production-ready systems.
This section teaches you how to evaluate, calibrate, and optimize your models so they perform reliably across different scenarios and deliver real business value.
What You’ll Master in This Part¶
- Comprehensive evaluation metrics beyond simple accuracy
- Robust cross-validation techniques for reliable performance estimation
- Systematic hyperparameter tuning strategies
- Probability calibration for trustworthy uncertainty estimates
- Cost-sensitive threshold selection for business-aligned decisions
Chapter Breakdown¶
| Chapter | Title | What You’ll Learn |
|---|---|---|
| 11 | Model Evaluation Metrics | Accuracy, precision, recall, F1, confusion matrix, ROC/PR curves, when metrics disagree |
| 12 | Cross-Validation & StratifiedKFold | Why CV matters, KFold vs Stratified, cross_validate, GridSearchCV, RandomizedSearchCV |
| 13 | Hyperparameter Tuning | Grid search vs random search, parameter space design, practical examples with SVM and RF |
| 14 | Probability Calibration | Why probabilities can lie, Platt scaling, isotonic regression, CalibratedClassifierCV |
| 15 | Choosing Decision Thresholds | Probabilities vs classes, cost-sensitive thresholds, F1 optimization, threshold tuning plots |
Why This Part Matters¶
You can train a thousand models, but without proper evaluation and tuning, you'll never know which one to trust.
This part will help you:
- Choose the right metrics for your specific problem (not just accuracy)
- Avoid overfitting through proper cross-validation
- Systematically improve model performance through hyperparameter optimization
- Generate reliable probability estimates for decision-making
- Align model predictions with real-world business costs and constraints
When it's time to deploy, you'll be able to say:
"This model doesn't just work—it works reliably, efficiently, and aligned with our objectives."