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 splits, where 70 % or 80 % of observations are held for training and the remainder for testing. This approach is straightforward and computationally inexpensive, making it suitable for large data sets or when quick assessments are needed. However, because performance can depend heavily on which observations belong to the test set, the simple holdout scheme may suffer from high variance and can be misleading if the split inadvertently creates a skewed or non‑representative test set.

To mitigate these issues, researchers often use repeated holdout or randomized resampling strategies. In repeated holdout, the data are randomly split multiple times, and performance metrics are averaged across runs, reducing the influence of any single split. Another strategy, sometimes called the duplicated holdout or “random subspace” method, creates multiple random training/test partitions and aggregates results. Though not as rigorous as cross‑validation, these enhanced holdout approaches balance computational efficiency with more reliable estimates of model generalisation.