Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the What you really need to know section for the big picture.

julearn.model_selection.StratifiedBootstrap

class julearn.model_selection.StratifiedBootstrap(n_splits=200, random_state=None)

Class-wise stratified bootstrap cross-validator.

Provides train/test indices to split data in train/test sets.

This cross-validation object returns stratified randomized folds. The folds are made by preserving the percentage of samples for each class in y in a binary or multiclass classification setting.

Parameters:

n_splitsint, default=200

Number of bootstrap iterations.

random_stateint or 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.

__init__(n_splits=200, random_state=None)

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.

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.

Notes

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.

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

Return 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.

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.

Examples using julearn.model_selection.StratifiedBootstrap

Confound Removal (model comparison)

Confound Removal (model comparison)