StratifiedKFold

Stratified K-Folds cross-validator.

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

This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class.

Constructors

constructor()

Signature

new StratifiedKFold(opts?: object): StratifiedKFold;

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. `Default Value` `false`

Returns

StratifiedKFold

Defined in: generated/model_selection/StratifiedKFold.ts:29 (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/StratifiedKFold.ts:108 (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/StratifiedKFold.ts:127 (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/StratifiedKFold.ts:162 (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/StratifiedKFold.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

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. Note that providing `y` is sufficient to generate the splits and hence `np.zeros(n\_samples)` may be used as a placeholder for `X` instead of actual training data.
`opts.groups?`	`any`	Always ignored, exists for compatibility.
`opts.y?`	`ArrayLike`	The target variable for supervised learning problems. Stratification is done based on the y labels.