Documentation
Classes
StratifiedKFold

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.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new StratifiedKFold(opts?: object): StratifiedKFold;

Parameters

NameTypeDescription
opts?object-
opts.n_splits?numberNumber of folds. Must be at least 2. Default Value 5
opts.random_state?numberWhen 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?booleanWhether 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:27 (opens in a new tab)

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

Defined in: generated/model_selection/StratifiedKFold.ts:24 (opens in a new tab)

_py

PythonBridge

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

id

string

Defined in: generated/model_selection/StratifiedKFold.ts:20 (opens in a new tab)

opts

any

Defined in: generated/model_selection/StratifiedKFold.ts:21 (opens in a new tab)

Accessors

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/model_selection/StratifiedKFold.ts:51 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/model_selection/StratifiedKFold.ts:55 (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:106 (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/StratifiedKFold.ts:123 (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/StratifiedKFold.ts:64 (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/StratifiedKFold.ts:166 (opens in a new tab)