Overfitting

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

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

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.

A parameterized mapping from inputs to outputs; includes architecture + learned parameters.

The learned numeric values of a model adjusted during training to minimize a loss function.

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

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