Term tag: hyperparameters

Cross-Validation (CV)

How cross-validation turns one lucky train/test split into a stable estimate, why 5 or 10 folds, and the leakage that quietly inflates every score.

Early Stopping

Early stopping halts training once validation loss stops improving, so the model generalises instead of memorising. How patience and best-weight restore work.

Hyperparameter

A setting you choose before training that gradient descent never updates — learning rate, batch size, layer count — and why a bad one caps the model.

Temperature

Temperature is the number that controls how random a language model's output is — it divides the logits before softmax, from near-deterministic to diverse.