Results for "bias"

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

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

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.