Selecting variables to include in a model
Comparing predictors
Selecting parameters in prediction function
All of the mentioned
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The correct answer is D. All of the mentioned.
Cross-validation is a resampling procedure for evaluating the performance of a statistical model on unseen data. It is used to estimate the error rate of a model and to select the best model among a set of candidate models.
Cross-validation can be used for the following purposes:
- Selecting variables to include in a model: Cross-validation can be used to select the best subset of variables to include in a model. This is done by splitting the data into a number of folds, and then training the model on each fold and evaluating it on the remaining folds. The 0 576 512">
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