holdoutmenetelmiä
Holdoutmenetelmiä, or holdout methods, are statistical techniques used primarily in machine learning and data science to assess the performance of predictive models. The basic idea is to divide a data set into two or more disjoint subsets: a training set, used to fit the model, and a test (or validation) set, used to evaluate its predictive accuracy. By ensuring that the test data have not been used during training, holdout methods aim to provide an unbiased estimate of how the model will generalise to new, unseen data.
The most common variant is a single split of the data, such as the 70/30 or 80/20
To mitigate these issues, researchers often use repeated holdout or randomized resampling strategies. In repeated holdout,