In questions $1-5,$ answer whether each statement is true or false regarding cross validation.

$1.$ The validation set approach tends to overestimate the test mean square error.

$2.$ 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.

$3.$ The purpose of splitting a dataset into a training subset and a validation subset is to lower the run$-$time of the training process.

$4.$ 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.

$5.$ Cross validation prevents knowledge about the test set from leaking into the model.