Crossvalidation helps prevent overfitting in machine learning models from "summary" of Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido
Cross-validation is a statistical method that is used to estimate the performance of a machine learning model. The goal of cross-validation is to assess how well a model will generalize to an independent data set. One common problem in machine learning is overfitting, where a model performs well on the training data but poorly on unseen data. This can happen when a model is too complex and captures noise in the training data. Cross-validation helps prevent overfitting by providing an estimate of how well a model will generalize to new data.
In cross-validation, the data is split into multiple subsets or folds. The model is trained on some of the fold...
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