dask_ml.model_selection.KFold

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.

Parameters:
n_splits : int, default=5

Number of folds. Must be at least 2.

shuffle : boolean, optional

Whether to shuffle the data before splitting into batches.

random_state : int, 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.

Methods

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)

Initialize self. See help(type(self)) for accurate signature.

get_n_splits(X=None, y=None, groups=None)

Returns the number of splitting iterations in the cross-validator

split(X, y=None, groups=None)

Generate indices to split data into training and test set.

Parameters:
X : array-like, shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

y : array-like, of length n_samples

The target variable for supervised learning problems.

groups : array-like, with shape (n_samples,), optional

Group labels for the samples used while splitting the dataset into train/test set.

Yields:
train : ndarray

The training set indices for that split.

test : ndarray

The testing set indices for that split.