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
threshold : float, optional
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.
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.
transform
(X)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.
y : any
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
X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
**fit_params : dict
Additional fit parameters.
- Returns
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
-
get_params
(deep=True)[source]¶ Get parameters for this estimator.
- Parameters
deep : bool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params : mapping of string to any
Parameter names mapped to their values.
-
get_support
(indices=False)[source]¶ Get a mask, or integer index, of the features selected
- Parameters
indices : boolean (default False)
If True, the return value will be an array of integers, rather than a boolean mask.
- Returns
support : array
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. Ifindices
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
X : array of shape [n_samples, n_selected_features]
The input samples.
- Returns
X_r : array of shape [n_samples, n_original_features]
X
with columns of zeros inserted where features would have been removed bytransform
.
-
set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params : dict
Estimator parameters.
- Returns
self : object
Estimator instance.