Confusion Matrix

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.

Of true negatives, the fraction correctly identified.

A conceptual framework describing error as the sum of systematic error (bias) and sensitivity to data (variance).

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.