Class: ShuffleSplit

Random permutation cross-validator.

Yields indices to split data into training and test sets.

Note: contrary to other cross-validation strategies, random splits do not guarantee that test sets across all folds will be mutually exclusive, and might include overlapping samples. However, this is still very likely for sizeable datasets.

Constructors

new ShuffleSplit()

new ShuffleSplit(opts?): ShuffleSplit

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.n_splits`?	`number`	Number of re-shuffling & splitting iterations.
`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 ShuffleSplit

Defined in generated/model_selection/ShuffleSplit.ts:29

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/model_selection/ShuffleSplit.ts:27
`_isInitialized`	`boolean`	`false`	generated/model_selection/ShuffleSplit.ts:26
`_py`	`PythonBridge`	`undefined`	generated/model_selection/ShuffleSplit.ts:25
`id`	`string`	`undefined`	generated/model_selection/ShuffleSplit.ts:22
`opts`	`any`	`undefined`	generated/model_selection/ShuffleSplit.ts:23

Accessors

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/model_selection/ShuffleSplit.ts:56

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

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

Returns Promise<void>

Defined in generated/model_selection/ShuffleSplit.ts:108

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

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

Returns Promise<any>

Defined in generated/model_selection/ShuffleSplit.ts:127

get_n_splits()

get_n_splits(opts): Promise<number>

Returns the number of splitting iterations in the cross-validator.

Parameters

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

Returns Promise<number>

Defined in generated/model_selection/ShuffleSplit.ts:161

init()

init(py): Promise<void>

Initializes the underlying Python resources.

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

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/model_selection/ShuffleSplit.ts:69

split()

split(opts): Promise<ArrayLike>

Generate indices to split data into training and test set.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.groups`?	`any`	Always ignored, exists for compatibility.
`opts.X`?	`ArrayLike`[]	Training data, where `n_samples` is the number of samples and `n_features` is the number of features.
`opts.y`?	`ArrayLike`	The target variable for supervised learning problems.

Returns Promise<ArrayLike>

Defined in generated/model_selection/ShuffleSplit.ts:203

ShrunkCovariance SimpleImputer