sklearn.model_selection.KFold  
class sklearn.model_selection.KFold(n_splits=5, *, 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_splitsint, default=5 

Number of folds. Must be at least 2.  Changed in version 0.22: n_splits default value changed from 3 to 5.   
shufflebool, default=False 

Whether to shuffle the data before splitting into batches. Note that the samples within each split will not be shuffled.  
random_stateint, RandomState instance or None, default=None 

When shuffle is True, random_state affects the ordering of the indices, which controls the randomness of each fold. Otherwise, this parameter has no effect. Pass an int for reproducible output across multiple function calls. See Glossary.      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.  
RepeatedKFold


Repeats K-Fold n times.    Notes The first n_samples % n_splits folds have size n_samples // n_splits + 1, other folds have size n_samples // n_splits, where n_samples is the number of samples. Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer. Examples >>> import numpy as np
>>> 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.    
get_n_splits(X=None, y=None, groups=None) [source]
 
Returns the number of splitting iterations in the cross-validator  Parameters 
 
Xobject 

Always ignored, exists for compatibility.  
yobject 

Always ignored, exists for compatibility.  
groupsobject 

Always ignored, exists for compatibility.    Returns 
 
n_splitsint 

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 
 
Xarray-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.  
yarray-like of shape (n_samples,), default=None 

The target variable for supervised learning problems.  
groupsarray-like of shape (n_samples,), default=None 

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

The training set indices for that split.  
testndarray 

The testing set indices for that split.     
 
 Examples using sklearn.model_selection.KFold
 
  Feature agglomeration vs. univariate selection  

  Gradient Boosting Out-of-Bag estimates  

  Nested versus non-nested cross-validation  

  Visualizing cross-validation behavior in scikit-learn  

  Cross-validation on diabetes Dataset Exercise