dask_ml.model_selection
.KFold¶
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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.
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get_n_splits
(X=None, y=None, groups=None)¶ Returns the number of splitting iterations in the cross-validator
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split
(X, y=None, groups=None)¶ Generate indices to split data into training and test set.
Parameters: - X : array-like of 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 shape (n_samples,)
The target variable for supervised learning problems.
- groups : array-like of shape (n_samples,), default=None
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.