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In questions 1 - 5 , answer whether each statement is true or false regarding cross validation. 1 . The validation set approach tends to
In questions answer whether each statement is true or false regarding cross validation.
The validation set approach tends to overestimate the test mean square error.
Consider a dataset that is evenly partitioned into two subsets: a training subset and a validation subset. If we build an algorithm that correctly predicts all outputs using the training subset, we are guaranteed to have it accurately predict all outputs using the validation subset.
The purpose of splitting a dataset into a training subset and a validation subset is to lower the runtime of the training process.
The correct procedure in cross validation is first training an algorithm on the complete dataset, then partitioning a portion of that dataset to test the algorithm.
Cross validation prevents knowledge about the test set from leaking into the model.
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