class dask_ml.model_selection.KFold(n_splits=5, shuffle=False, random_state=None)

K-Folds cross-validator

Provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).

Each fold is then used once as a validation while the k - 1 remaining folds form the training set.

n_splitsint, default=5

Number of folds. Must be at least 2.

shuffleboolean, optional

Whether to shuffle the data before splitting into batches.

random_stateint, RandomState instance or None, optional, default=None

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when shuffle == True.


get_n_splits([X, y, groups])

Returns the number of splitting iterations in the cross-validator

split(X[, y, groups])

Generate indices to split data into training and test set.

__init__(n_splits=5, shuffle=False, random_state=None)