H13-311_V3.5 · Question #202
K Folding cross-validation refers to dividing the test data set into K Sub-data sets.
The correct answer is B. FALSE. B is correct because K-Fold cross-validation divides the training dataset (or the full available dataset) into K equal subsets - not the test set. In each iteration, K-1 folds are used for training and the remaining 1 fold is used for validation, rotating until every fold has…
Question
K Folding cross-validation refers to dividing the test data set into K Sub-data sets.
Options
- ATRUE
- BFALSE
How the community answered
(28 responses)- A29% (8)
- B71% (20)
Explanation
B is correct because K-Fold cross-validation divides the training dataset (or the full available dataset) into K equal subsets - not the test set. In each iteration, K-1 folds are used for training and the remaining 1 fold is used for validation, rotating until every fold has served as the validation set once.
Why A (TRUE) is wrong: The statement misidentifies which data is being split. The test set is typically a separate, held-out portion of data that is never touched during cross-validation; splitting it would defeat its purpose as an unbiased final evaluation.
Memory tip: Think of the K folds as slices of your training "pie" - you rotate which slice you taste-test while training on the rest. The test set is a completely separate dish you only touch at the very end.
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