Specificity

Harmonic mean of precision and recall; useful when balancing false positives/negatives matters.

Plots true positive rate vs false positive rate across thresholds; summarizes separability.

Scalar summary of ROC; measures ranking ability, not calibration.

The degree to which predicted probabilities match true frequencies (e.g., 0.8 means ~80% correct).

A table summarizing classification outcomes, foundational for metrics like precision, recall, specificity.

Fraction of correct predictions; can be misleading on imbalanced datasets.

Of predicted positives, the fraction that are truly positive; sensitive to false positives.

Of true positives, the fraction correctly identified; sensitive to false negatives.