ristikkäisvalidointia

Ristikkäisvalidointi, known in English as cross-validation, is a statistical resampling technique used to evaluate and improve the performance of machine learning models. It provides a more robust estimate of how a model will generalize to unseen data compared to a simple train-test split.

The fundamental idea behind ristikkäisvalidointi is to partition the available dataset into several subsets, or "folds." The model is then trained and evaluated multiple times. In each iteration, one fold is held out as the testing set, while the remaining folds are used for training the model. This process is repeated for each fold, ensuring that every data point gets to be in the testing set exactly once.

The results from these multiple training and testing cycles are then averaged to obtain a single performance metric, such as accuracy, precision, or recall. This averaged metric is considered a more reliable indicator of the model's predictive power because it has been tested on various subsets of the data.

Common types of ristikkäisvalidointi include k-fold cross-validation, where the data is divided into 'k' equal-sized folds, and leave-one-out cross-validation (LOOCV), a special case of k-fold where k is equal to the number of data points. Ristikkäisvalidointi is crucial for detecting overfitting, where a model performs well on the training data but poorly on new, unseen data. By systematically evaluating the model across different data partitions, it helps in selecting models that generalize better.