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 divided into k equal-sized subsets or "folds." The model is then trained k times. Each time, one fold is used as the validation set, and the remaining k-1 folds are used for training. This process ensures that every data point is used for both training and validation exactly once. The performance metrics from each of the k training iterations are averaged to provide a more robust estimate of the model's performance.

Ristikkäisvalidointi helps in preventing overfitting, a situation where a model learns the training data too well, including its noise and outliers, and performs poorly on unseen data. By using different subsets for validation, it provides a better understanding of how the model is likely to perform in real-world scenarios. The choice of k, the number of folds, is a hyperparameter. Common values for k are 5 or 10, but it can be adjusted based on the dataset size and computational resources. While more folds generally lead to a more accurate estimate, they also increase computation time.