Results for "probably approximately correct"

A model is PAC-learnable if it can, with high probability, learn an approximately correct hypothesis from finite samples.

A theoretical framework analyzing what classes of functions can be learned, how efficiently, and with what guarantees.

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

A formal privacy framework ensuring outputs do not reveal much about any single individual’s data contribution.

Fraction of correct predictions; can be misleading on imbalanced datasets.

The degree to which predicted probabilities match true frequencies (e.g., 0.8 means ~80% correct).

Coordinating tools, models, and steps (retrieval, calls, validation) to deliver reliable end-to-end behavior.

Control without feedback after execution begins.

Learning from data generated by a different policy.