Phase 6: Evaluation & Tuning
Phase 6: Evaluation & Tuning
In the final phase, we rigorously test the model to ensure it is reliable, fair, and optimal.
🟢 Level 1: Advanced Metrics
Beyond simple Accuracy.
1. Classification Metrics
- Precision vs. Recall: The trade-off between missing a positive and predicting a false positive.
- F1-Score: Harmonic mean of precision and recall.
- ROC-AUC: Area under the Receiver Operating Characteristic curve.
2. Regression Metrics
- MAE: Mean Absolute Error (Robust to outliers).
- RMSE: Root Mean Squared Error (Punishes large errors).
🟡 Level 2: Hyperparameter Tuning
Searching for the “Best” settings for your model.
3. Search Strategies
- Grid Search: Exhaustive search (Slow).
- Random Search: Faster and often just as good.
- Bayesian Optimization (Optuna): The gold standard for modern ML.
🔴 Level 3: Fairness & Bias
Just because a model is accurate doesn’t mean it’s “Correct.”
4. Ethics in ML
- Bias Detection: Auditing the model for demographic parity.
- Data Leakage Check: Ensuring “future” information isn’t in the training data.
Always use Cross-Validation (K-Fold) during evaluation. A single train/test split can be lucky or unlucky. 5-fold cross-validation gives you a much better estimate of the model’s true performance.