kfoldristivalidointia
Kfoldristivalidointia, often referred to as k-fold cross-validation, is a resampling technique used to evaluate machine learning models on a limited data sample. The method involves partitioning the dataset into k mutually exclusive subsets or folds. The model is then trained k times. In each training iteration, one of the k folds is held out as the test set, and the remaining k-1 folds are used for training the model. This process is repeated k times, with each fold serving as the test set exactly once.
The primary purpose of k-fold cross-validation is to provide a more robust estimate of the model's performance
The choice of k is a hyperparameter that needs to be selected. Common values for k include