Confusion Matrix

Summary

A table breaking predictions into true positives, false positives, true negatives and false negatives — the raw material every classification metric above is calculated from.

How to Learn This

1Build and interpret a confusion matrix for a model you've trained.
2Learn to identify which error type (false positive or false negative) matters more for your use case.
3Practice explaining a confusion matrix to a non-technical stakeholder.

Classification Metrics (Accuracy, Precision, Recall, F1)

ROC-AUC Curve

More topics in Model Evaluation & Tuning

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

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