Bias–Variance Tradeoff

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.

A function measuring prediction error (and sometimes calibration), guiding gradient-based optimization.

Techniques that discourage overly complex solutions to improve generalization (reduce overfitting).

When a model fits noise/idiosyncrasies of training data and performs poorly on unseen data.

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