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  "body": "\n## Summary\n\nCross-validation estimates how well a fitted model predicts new data by repeatedly fitting it on part of the data and testing it on the rest.\nEach observation is used for testing exactly once, with a model that did not see it.\nIt is the standard tool to choose tuning parameters, such as a bandwidth or a penalty, and to compare predictive models.\n\n## Explanations\n\n### L1 human\n\nChecking a model on the same data that built it is too optimistic, like grading students on questions they saw in advance.\nCross-validation hides a part of the data, fits the model on the rest, and checks the predictions on the hidden part.\nIn $5$-fold cross-validation, the data are cut into $5$ pieces and each piece is hidden once.\nThe average error over the $5$ rounds estimates the error on new data.\n",
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