InstanceHardnessCV

class imblearn.model_selection.InstanceHardnessCV(estimator, *, n_splits=5, pos_label=None)

Instance-hardness cross-validation splitter.

Cross-validation splitter that distributes samples with large instance hardness equally over the folds. The instance hardness is internally estimated by using estimator and stratified cross-validation.

Read more in the User Guide.

Parameters:

estimatorestimator object

Classifier to be used to estimate instance hardness of the samples. This classifier should implement predict_proba.

n_splitsint, default=5

Number of folds. Must be at least 2.

pos_labelint, float, bool or str, default=None

The class considered the positive class when selecting the probability representing the instance hardness. If None, the positive class is automatically inferred by the estimator as estimator.classes_[1].

Examples

>>> from imblearn.model_selection import InstanceHardnessCV
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import cross_validate
>>> from sklearn.linear_model import LogisticRegression
>>> X, y = make_classification(weights=[0.9, 0.1], class_sep=2,
... n_informative=3, n_redundant=1, flip_y=0.05, n_samples=1000, random_state=10)
>>> estimator = LogisticRegression()
>>> ih_cv = InstanceHardnessCV(estimator)
>>> cv_result = cross_validate(estimator, X, y, cv=ih_cv)
>>> print(f"Standard deviation of test_scores: {cv_result['test_score'].std():.3f}")
Standard deviation of test_scores: 0.00...

Methods

get_metadata_routing()	Get metadata routing of this object.
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_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_n_splits(X=None, y=None, groups=None)

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, groups=None)

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,)

The target variable for supervised learning problems.

groupsobject

Always ignored, exists for compatibility.

Yields:

trainndarray

The training set indices for that split.

testndarray

The testing set indices for that split.

Examples using imblearn.model_selection.InstanceHardnessCV

Distribute hard-to-classify datapoints over CV folds

Distribute hard-to-classify datapoints over CV folds