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Classes
StratifiedGroupKFold

StratifiedGroupKFold

Stratified K-Folds iterator variant with non-overlapping groups.

This cross-validation object is a variation of StratifiedKFold attempts to return stratified folds with non-overlapping groups. The folds are made by preserving the percentage of samples for each class.

Each group will appear exactly once in the test set across all folds (the number of distinct groups has to be at least equal to the number of folds).

The difference between GroupKFold and StratifiedGroupKFold is that the former attempts to create balanced folds such that the number of distinct groups is approximately the same in each fold, whereas StratifiedGroupKFold attempts to create folds which preserve the percentage of samples for each class as much as possible given the constraint of non-overlapping groups between splits.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new StratifiedGroupKFold(opts?: object): StratifiedGroupKFold;

Parameters

NameTypeDescription
opts?object-
opts.n_splits?numberNumber of folds. Must be at least 2. Default Value 5
opts.random_state?numberWhen shuffle is true, random\_state affects the ordering of the indices, which controls the randomness of each fold for each class. Otherwise, leave random\_state as undefined. Pass an int for reproducible output across multiple function calls. See Glossary.
opts.shuffle?booleanWhether to shuffle each class’s samples before splitting into batches. Note that the samples within each split will not be shuffled. This implementation can only shuffle groups that have approximately the same y distribution, no global shuffle will be performed. Default Value false

Returns

StratifiedGroupKFold

Defined in: generated/model_selection/StratifiedGroupKFold.ts:29 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/model_selection/StratifiedGroupKFold.ts:27 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/model_selection/StratifiedGroupKFold.ts:26 (opens in a new tab)

_py

PythonBridge

Defined in: generated/model_selection/StratifiedGroupKFold.ts:25 (opens in a new tab)

id

string

Defined in: generated/model_selection/StratifiedGroupKFold.ts:22 (opens in a new tab)

opts

any

Defined in: generated/model_selection/StratifiedGroupKFold.ts:23 (opens in a new tab)

Accessors

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/model_selection/StratifiedGroupKFold.ts:53 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/model_selection/StratifiedGroupKFold.ts:57 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/model_selection/StratifiedGroupKFold.ts:112 (opens in a new tab)

get_n_splits()

Returns the number of splitting iterations in the cross-validator

Signature

get_n_splits(opts: object): Promise<number>;

Parameters

NameTypeDescription
optsobject-
opts.X?anyAlways ignored, exists for compatibility.
opts.groups?anyAlways ignored, exists for compatibility.
opts.y?anyAlways ignored, exists for compatibility.

Returns

Promise<number>

Defined in: generated/model_selection/StratifiedGroupKFold.ts:129 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/model_selection/StratifiedGroupKFold.ts:66 (opens in a new tab)

split()

Generate indices to split data into training and test set.

Signature

split(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training data, where n\_samples is the number of samples and n\_features is the number of features.
opts.groups?ArrayLikeGroup labels for the samples used while splitting the dataset into train/test set.
opts.y?ArrayLikeThe target variable for supervised learning problems.

Returns

Promise<ArrayLike>

Defined in: generated/model_selection/StratifiedGroupKFold.ts:176 (opens in a new tab)