Results for "bias"

A structured collection of examples used to train/evaluate models; quality, bias, and coverage often dominate outcomes.

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

Separating data into training (fit), validation (tune), and test (final estimate) to avoid leakage and optimism bias.

Systematic differences in model outcomes across groups; arises from data, labels, and deployment context.

Tendency to trust automated suggestions even when incorrect; mitigated by UI design, training, and checks.

Systematic error introduced by simplifying assumptions in a learning algorithm.

Built-in assumptions guiding learning efficiency and generalization.

Differences between training and inference conditions.

Unequal performance across demographic groups.