Accuracy

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

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

Of true negatives, the fraction correctly identified.

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

How well a model performs on new data drawn from the same (or similar) distribution as training.

When information from evaluation data improperly influences training, inflating reported performance.

A robust evaluation technique that trains/evaluates across multiple splits to estimate performance variability.

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