Classification Metrics
← Back to Model Evaluation
Metrics for evaluating models that predict discrete categories. Different metrics emphasize different aspects of performance; the right choice depends on the problem and business context.
Key Metrics
- Accuracy — fraction of correct predictions (misleading for imbalanced data)
- Precision — of predicted positives, how many are correct
- Recall — of actual positives, how many were found
- F1-Score — harmonic mean of precision and recall
- AUC-ROC — area under receiver operating characteristic curve
- Confusion Matrix — full breakdown of TP, FP, TN, FN
Related
- Regression Metrics (continuous outputs)
- Bias-Variance Tradeoff (precision-recall tradeoff)