Ristikkäisvalidointi
Ristikkäisvalidointi, often translated as cross-validation, is a resampling technique used to evaluate machine learning models on a limited data sample. Its primary purpose is to assess how well a model will generalize to an independent dataset.
The most common form of ristikkäisvalidointi is k-fold ristikkäisvalidointi. In this method, the dataset is randomly
Ristikkäisvalidointi helps in preventing overfitting, a situation where a model learns the training data too well,