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_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.
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)¶