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

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

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

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

True

False

Question at position $2$

$2$

$1$ point

Question at position $2$

$[$Refer to prompt $1]$ 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.

$[$Refer to prompt $1]$ 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.

True

False

Question at position $3$

$3$

$1$ point

Question at position $3$

$[$Refer to prompt $1]$

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

$[$Refer to prompt $1]$

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

True

False

Question at position $4$

$4$

$1$ point

Question at position $4$

$[$Refer to prompt $1]$ 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.

$[$Refer to prompt $1]$ 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.

True

False

Question at position $5$

$5$

$1$ point

Question at position $5$

$[$Refer to prompt $1]$ Cross validation prevents knowledge about the test set from leaking into the model.

$[$Refer to prompt $1]$ Cross validation prevents knowledge about the test set from leaking into the model.

True

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

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

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

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

True

False

Question at position $2$

$2$

$1$ point

Question at position $2$

$[$Refer to prompt $1]$ 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.

$[$Refer to prompt $1]$ 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.

True

False

Question at position $3$

$3$

$1$ point

Question at position $3$

$[$Refer to prompt $1]$

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

$[$Refer to prompt $1]$

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

True

False

Question at position $4$

$4$

$1$ point

Question at position $4$

$[$Refer to prompt $1]$ 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.

$[$Refer to prompt $1]$ 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.

True

False

Question at position $5$

$5$

$1$ point

Question at position $5$

$[$Refer to prompt $1]$ Cross validation prevents knowledge about the test set from leaking into the model.

$[$Refer to prompt $1]$ Cross validation prevents knowledge about the test set from leaking into the model.

True

False