# sklearn.feature_selection.VarianceThreshold¶

class sklearn.feature_selection.VarianceThreshold(threshold=0.0)[source]

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.

Parameters
thresholdfloat, default=0

Features 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.

Attributes
variances_array, shape (n_features,)

Variances of individual features.

Notes

Allows NaN in the input. Raises ValueError if no feature in X meets the variance threshold.

Examples

The following dataset has integer features, two of which are the same in every sample. These are removed with the default setting for threshold:

>>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
>>> selector = VarianceThreshold()
>>> selector.fit_transform(X)
array([[2, 0],
[1, 4],
[1, 1]])


Methods

 fit(X[, y]) Learn empirical variances from X. fit_transform(X[, y]) Fit to data, then transform it. get_params([deep]) Get parameters for this estimator. get_support([indices]) Get a mask, or integer index, of the features selected Reverse the transformation operation set_params(**params) Set the parameters of this estimator. Reduce X to the selected features.
fit(X, y=None)[source]

Learn empirical variances from X.

Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)

Sample vectors from which to compute variances.

yany, default=None

Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline.

Returns
self
fit_transform(X, y=None, **fit_params)[source]

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Returns
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsdict

Parameter names mapped to their values.

get_support(indices=False)[source]

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

Parameters
indicesbool, default=False

If True, the return value will be an array of integers, rather than a boolean mask.

Returns
supportarray

An index that selects the retained features from a feature vector. If indices is False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

inverse_transform(X)[source]

Reverse the transformation operation

Parameters
Xarray of shape [n_samples, n_selected_features]

The input samples.

Returns
X_rarray of shape [n_samples, n_original_features]

X with columns of zeros inserted where features would have been removed by transform.

set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters
**paramsdict

Estimator parameters.

Returns
selfestimator instance

Estimator instance.

transform(X)[source]

Reduce X to the selected features.

Parameters
Xarray of shape [n_samples, n_features]

The input samples.

Returns
X_rarray of shape [n_samples, n_selected_features]

The input samples with only the selected features.