ROC-AUC Curve

Summary

A curve plotting true positive rate against false positive rate across different classification thresholds — AUC (area under the curve) summarizes overall model discriminative power in one number.

How to Learn This

1Plot an ROC curve for a trained classification model.
2Learn what an AUC of 0.5 versus 1.0 versus a specific value like 0.85 actually means.
3Understand ROC-AUC can be misleading on severely imbalanced datasets — check precision-recall curves too.

Confusion Matrix

Regression Metrics (MAE, RMSE, R²)

More topics in Model Evaluation & Tuning

Classification Metrics (Accuracy, Precision, Recall, F1)Confusion Matrix Regression Metrics (MAE, RMSE, R²)Hyperparameter Tuning (Grid / Random Search)Regularization (L1 / L2)Ensemble Methods Model Selection Strategy

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