Cross-Validation

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.

When a model cannot capture underlying structure, performing poorly on both training and test data.

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.