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Classes
StratifiedShuffleSplit

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.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new StratifiedShuffleSplit(opts?: object): StratifiedShuffleSplit;

Parameters

NameTypeDescription
opts?object-
opts.n_splits?numberNumber of re-shuffling & splitting iterations. Default Value 10
opts.random_state?numberControls the randomness of the training and testing indices produced. Pass an int for reproducible output across multiple function calls. See Glossary.
opts.test_size?numberIf 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?numberIf 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:29 (opens in a new tab)

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

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

_py

PythonBridge

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

id

string

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

opts

any

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

Accessors

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/model_selection/StratifiedShuffleSplit.ts:56 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/model_selection/StratifiedShuffleSplit.ts:60 (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:115 (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/StratifiedShuffleSplit.ts:132 (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/StratifiedShuffleSplit.ts:69 (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. 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?anyAlways ignored, exists for compatibility.
opts.y?ArrayLikeThe target variable for supervised learning problems. Stratification is done based on the y labels.

Returns

Promise<ArrayLike>

Defined in: generated/model_selection/StratifiedShuffleSplit.ts:179 (opens in a new tab)