Crossvalidation ensures the generalization of models from "summary" of Machine Learning by Ethem Alpaydin
Crossvalidation is a technique used to ensure the generalization of models. When we build a model using a training set and then evaluate it using a test set, we are assuming that the test set is representative of the population. However, in practice, this may not always be the case. The test set may be too small, leading to high variance in the performance of the model. Crossvalidation addresses this issue by partitioning the data into multiple subsets and using each subset as both a training set and a test set. By averaging the performance of the model over multiple iterations, we can get a more accurate estimate of how well the model will perform on unseen data. One common method of crossvalidation is k-f...
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