Documentation
Classes
VarianceThreshold

VarianceThreshold

Feature selector that removes all low-variance features.

This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new VarianceThreshold(opts?: object): VarianceThreshold;

Parameters

NameTypeDescription
opts?object-
opts.threshold?numberFeatures with a training-set variance lower than this threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples. Default Value 0

Returns

VarianceThreshold

Defined in: generated/feature_selection/VarianceThreshold.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/feature_selection/VarianceThreshold.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/feature_selection/VarianceThreshold.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/feature_selection/VarianceThreshold.ts:21 (opens in a new tab)

id

string

Defined in: generated/feature_selection/VarianceThreshold.ts:18 (opens in a new tab)

opts

any

Defined in: generated/feature_selection/VarianceThreshold.ts:19 (opens in a new tab)

Accessors

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/feature_selection/VarianceThreshold.ts:441 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/feature_selection/VarianceThreshold.ts:414 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/feature_selection/VarianceThreshold.ts:37 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/feature_selection/VarianceThreshold.ts:41 (opens in a new tab)

variances_

Variances of individual features.

Signature

variances_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/feature_selection/VarianceThreshold.ts:387 (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/feature_selection/VarianceThreshold.ts:92 (opens in a new tab)

fit()

Learn empirical variances from X.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?anyData from which to compute variances, where n\_samples is the number of samples and n\_features is the number of features.
opts.y?anyIgnored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline.

Returns

Promise<any>

Defined in: generated/feature_selection/VarianceThreshold.ts:109 (opens in a new tab)

fit_transform()

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.

Signature

fit_transform(opts: object): Promise<any[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Input samples.
opts.fit_params?anyAdditional fit parameters.
opts.y?ArrayLikeTarget values (undefined for unsupervised transformations).

Returns

Promise<any[]>

Defined in: generated/feature_selection/VarianceThreshold.ts:151 (opens in a new tab)

get_feature_names_out()

Mask feature names according to selected features.

Signature

get_feature_names_out(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.input_features?anyInput features.

Returns

Promise<any>

Defined in: generated/feature_selection/VarianceThreshold.ts:202 (opens in a new tab)

get_support()

Get a mask, or integer index, of the features selected.

Signature

get_support(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.indices?booleanIf true, the return value will be an array of integers, rather than a boolean mask. Default Value false

Returns

Promise<any>

Defined in: generated/feature_selection/VarianceThreshold.ts:240 (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/feature_selection/VarianceThreshold.ts:50 (opens in a new tab)

inverse_transform()

Reverse the transformation operation.

Signature

inverse_transform(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?anyThe input samples.

Returns

Promise<any>

Defined in: generated/feature_selection/VarianceThreshold.ts:277 (opens in a new tab)

set_output()

Set output container.

See Introducing the set_output API for an example on how to use the API.

Signature

set_output(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.transform?"default" | "pandas"Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/feature_selection/VarianceThreshold.ts:317 (opens in a new tab)

transform()

Reduce X to the selected features.

Signature

transform(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?anyThe input samples.

Returns

Promise<any>

Defined in: generated/feature_selection/VarianceThreshold.ts:352 (opens in a new tab)