sklearn.model_selection
.LeavePOut¶
- class sklearn.model_selection.LeavePOut(p)[source]¶
Leave-P-Out cross-validator
Provides train/test indices to split data in train/test sets. This results in testing on all distinct samples of size p, while the remaining n - p samples form the training set in each iteration.
Note:
LeavePOut(p)
is NOT equivalent toKFold(n_splits=n_samples // p)
which creates non-overlapping test sets.Due to the high number of iterations which grows combinatorically with the number of samples this cross-validation method can be very costly. For large datasets one should favor
KFold
,StratifiedKFold
orShuffleSplit
.Read more in the User Guide.
- Parameters:
- pint
Size of the test sets. Must be strictly less than the number of samples.
Examples
>>> import numpy as np >>> from sklearn.model_selection import LeavePOut >>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]]) >>> y = np.array([1, 2, 3, 4]) >>> lpo = LeavePOut(2) >>> lpo.get_n_splits(X) 6 >>> print(lpo) LeavePOut(p=2) >>> for i, (train_index, test_index) in enumerate(lpo.split(X)): ... print(f"Fold {i}:") ... print(f" Train: index={train_index}") ... print(f" Test: index={test_index}") Fold 0: Train: index=[2 3] Test: index=[0 1] Fold 1: Train: index=[1 3] Test: index=[0 2] Fold 2: Train: index=[1 2] Test: index=[0 3] Fold 3: Train: index=[0 3] Test: index=[1 2] Fold 4: Train: index=[0 2] Test: index=[1 3] Fold 5: Train: index=[0 1] Test: index=[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, y=None, groups=None)[source]¶
Returns the number of splitting iterations in the cross-validator
- Parameters:
- Xarray-like of shape (n_samples, n_features)
Training data, where
n_samples
is the number of samples andn_features
is the number of features.- yobject
Always ignored, exists for compatibility.
- groupsobject
Always ignored, exists for compatibility.
- 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 andn_features
is the number of features.- yarray-like of shape (n_samples,)
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.