ShuffleSplit #

classsklearn.model_selection.ShuffleSplit(n_splits=10,*,test_size=None,train_size=None,random_state=None)[source]#

Random permutation cross-validator.

Yields indices to split data into training and test sets.

Note: contrary to other cross-validation strategies, random splitsdo not guarantee that test sets across all folds will be mutually exclusive,and might include overlapping samples. However, this is still very likely forsizeable datasets.

Read more in theUser Guide.

For visualisation of cross-validation behaviour andcomparison between common scikit-learn split methodsrefer toVisualizing cross-validation behavior in scikit-learn

Parameters:

n_splitsint, default=10: Number of re-shuffling & splitting iterations.
test_sizefloat or int, default=None: If float, should be between 0.0 and 1.0 and represent the proportionof the dataset to include in the test split. If int, represents theabsolute number of test samples. If None, the value is set to thecomplement of the train size. Iftrain_size is also None, it willbe set to 0.1.
train_sizefloat or int, default=None: If float, should be between 0.0 and 1.0 and represent theproportion of the dataset to include in the train split. Ifint, represents the absolute number of train samples. If None,the value is automatically set to the complement of the test size.
random_stateint, RandomState instance or None, default=None: Controls the randomness of the training and testing indices produced.Pass an int for reproducible output across multiple function calls.SeeGlossary.

Examples

>>>importnumpyasnp>>>fromsklearn.model_selectionimportShuffleSplit>>>X=np.array([[1,2],[3,4],[5,6],[7,8],[3,4],[5,6]])>>>y=np.array([1,2,1,2,1,2])>>>rs=ShuffleSplit(n_splits=5,test_size=.25,random_state=0)>>>rs.get_n_splits(X)5>>>print(rs)ShuffleSplit(n_splits=5, random_state=0, test_size=0.25, train_size=None)>>>fori,(train_index,test_index)inenumerate(rs.split(X)):...print(f"Fold{i}:")...print(f"  Train: index={train_index}")...print(f"  Test:  index={test_index}")Fold 0:  Train: index=[1 3 0 4]  Test:  index=[5 2]Fold 1:  Train: index=[4 0 2 5]  Test:  index=[1 3]Fold 2:  Train: index=[1 2 4 0]  Test:  index=[3 5]Fold 3:  Train: index=[3 4 1 0]  Test:  index=[5 2]Fold 4:  Train: index=[3 5 1 0]  Test:  index=[2 4]>>># Specify train and test size>>>rs=ShuffleSplit(n_splits=5,train_size=0.5,test_size=.25,...random_state=0)>>>fori,(train_index,test_index)inenumerate(rs.split(X)):...print(f"Fold{i}:")...print(f"  Train: index={train_index}")...print(f"  Test:  index={test_index}")Fold 0:  Train: index=[1 3 0]  Test:  index=[5 2]Fold 1:  Train: index=[4 0 2]  Test:  index=[1 3]Fold 2:  Train: index=[1 2 4]  Test:  index=[3 5]Fold 3:  Train: index=[3 4 1]  Test:  index=[5 2]Fold 4:  Train: index=[3 5 1]  Test:  index=[2 4]

get_metadata_routing()[source]#

Get metadata routing of this object.

Please checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest: AMetadataRequest encapsulatingrouting information.

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, wheren_samples is the number of samplesandn_features is the number of features.
yarray-like of shape (n_samples,): The target variable for supervised learning problems.
groupsobject: Always ignored, exists for compatibility.

Yields: