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Crossvalidation helps prevent overfitting by testing the model on multiple subsets of the data from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Crossvalidation is an important technique in data science that helps prevent overfitting. Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern. This can lead to poor performance on new, unseen data. Crossvalidation helps address this issue by testing the model on multiple subsets of the data. By splitting the data into multiple subsets or folds, crossvalidation allows the model to be trained on one subset and tested on another. This process i...
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