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.

Constructors

constructor()

Signature

new StratifiedGroupKFold(opts?: object): StratifiedGroupKFold;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.n_splits?`	`number`	Number of folds. Must be at least 2. `Default Value` `5`
`opts.random_state?`	`number`	When `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?`	`boolean`	Whether 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:31 (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:114 (opens in a new tab)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.routing?`	`any`	A `MetadataRequest` encapsulating routing information.

Returns

Promise<any>

Defined in: generated/model_selection/StratifiedGroupKFold.ts:133 (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

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	Always ignored, exists for compatibility.
`opts.groups?`	`any`	Always ignored, exists for compatibility.
`opts.y?`	`any`	Always ignored, exists for compatibility.

Returns

Promise<number>

Defined in: generated/model_selection/StratifiedGroupKFold.ts:171 (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

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/model_selection/StratifiedGroupKFold.ts:68 (opens in a new tab)

set_split_request()

Request metadata passed to the split method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

set_split_request(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.groups?`	`string` \| `boolean`	Metadata routing for `groups` parameter in `split`.

Returns

Promise<any>

Defined in: generated/model_selection/StratifiedGroupKFold.ts:222 (opens in a new tab)

split()

Generate indices to split data into training and test set.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Training data, where `n\_samples` is the number of samples and `n\_features` is the number of features.
`opts.groups?`	`ArrayLike`	Group labels for the samples used while splitting the dataset into train/test set.
`opts.y?`	`ArrayLike`	The target variable for supervised learning problems.