ml-k-fold-cross-validation

IN premise — entries/2026/06/21/wiki-Machine_learning-chunk-5.md

Created 2026-06-21T09:55:51+00:00

K-fold cross-validation partitions data into K subsets, running K experiments where each fold is used once for evaluation and K-1 folds for training, ensuring all data is used for both

Dependents

These beliefs depend on this one:

• IN ml-three-evaluation-methodologies-tradeoff — ML model evaluation has three standard methodologies — holdout splitting (simple but data-wasteful), k-fold cross-validation (balanced but k-times more expensive), and bootstrap sampling (statistically rich but with replacement effects) — each trading computational cost for estimation reliability.