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, 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(self, X[, y])Learn empirical variances from X.
fit_transform(self, X[, y])Fit to data, then transform it.
get_params(self[, deep])Get parameters for this estimator.
get_support(self[, indices])Get a mask, or integer index, of the features selected
inverse_transform(self, X)Reverse the transformation operation
set_params(self, \*\*params)Set the parameters of this estimator.
transform(self, X)Reduce X to the selected features.
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__init__(self, threshold=0.0)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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fit(self, 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
Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline.
- Returns
- self
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fit_transform(self, 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
- Xnumpy array of shape [n_samples, n_features]
Training set.
- ynumpy array of shape [n_samples]
Target values.
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newnumpy array of shape [n_samples, n_features_new]
Transformed array.
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get_params(self, 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
- paramsmapping of string to any
Parameter names mapped to their values.
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get_support(self, indices=False)[source]¶ Get a mask, or integer index, of the features selected
- Parameters
- indicesboolean (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
indicesis False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. Ifindicesis True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.
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inverse_transform(self, 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]
Xwith columns of zeros inserted where features would have been removed bytransform.
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set_params(self, **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
- **paramsdict
Estimator parameters.
- Returns
- selfobject
Estimator instance.