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  
inverse_transform(X) 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.  
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 

Additional fit parameters.    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.