StratifiedShuffleSplit

Stratified ShuffleSplit cross-validator

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

This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified randomized folds. The folds are made by preserving the percentage of samples for each class.

Note: like the ShuffleSplit strategy, stratified random splits do not guarantee that all folds will be different, although this is still very likely for sizeable datasets.

Constructors

constructor()

Signature

new StratifiedShuffleSplit(opts?: object): StratifiedShuffleSplit;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.n_splits?`	`number`	Number of re-shuffling & splitting iterations. `Default Value` `10`
`opts.random_state?`	`number`	Controls the randomness of the training and testing indices produced. Pass an int for reproducible output across multiple function calls. See Glossary.
`opts.test_size?`	`number`	If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If `undefined`, the value is set to the complement of the train size. If `train\_size` is also `undefined`, it will be set to 0.1.
`opts.train_size?`	`number`	If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If `undefined`, the value is automatically set to the complement of the test size.

Returns

StratifiedShuffleSplit

Defined in: generated/model_selection/StratifiedShuffleSplit.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/StratifiedShuffleSplit.ts:117 (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/StratifiedShuffleSplit.ts:136 (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/StratifiedShuffleSplit.ts:174 (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/StratifiedShuffleSplit.ts:71 (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.