-
class sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None)
[source] -
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.
Read more in the User Guide.
Parameters: n_splits : int, default=3
Number of folds. Must be at least 2.
shuffle : boolean, optional
Whether to shuffle the data before splitting into batches.
random_state : None, int or RandomState
When shuffle=True, pseudo-random number generator state used for shuffling. If None, use default numpy RNG for shuffling.
See also
-
StratifiedKFold
- Takes group information into account to avoid building folds with imbalanced class distributions (for binary or multiclass classification tasks).
-
GroupKFold
- K-fold iterator variant with non-overlapping groups.
Notes
The first
n_samples % n_splits
folds have sizen_samples // n_splits + 1
, other folds have sizen_samples // n_splits
, wheren_samples
is the number of samples.Examples
>>> from sklearn.model_selection import KFold >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([1, 2, 3, 4]) >>> kf = KFold(n_splits=2) >>> kf.get_n_splits(X) 2 >>> print(kf) KFold(n_splits=2, random_state=None, shuffle=False) >>> for train_index, test_index in kf.split(X): ... print("TRAIN:", train_index, "TEST:", test_index) ... X_train, X_test = X[train_index], X[test_index] ... y_train, y_test = y[train_index], y[test_index] TRAIN: [2 3] TEST: [0 1] TRAIN: [0 1] TEST: [2 3]
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=3, shuffle=False, random_state=None)
[source]
-
get_n_splits(X=None, y=None, groups=None)
[source] -
Returns the number of splitting iterations in the cross-validator
Parameters: X : object
Always ignored, exists for compatibility.
y : object
Always ignored, exists for compatibility.
groups : object
Always ignored, exists for compatibility.
Returns: n_splits : int
Returns the number of splitting iterations in the cross-validator.
-
split(X, y=None, groups=None)
[source] -
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, shape (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.
Returns: train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.
-
model_selection.KFold()
Examples using
2017-01-15 04:24:11
Please login to continue.