sklearn.feature_selection
.SelectFwe¶
- class sklearn.feature_selection.SelectFwe(score_func=<function f_classif>, *, alpha=0.05)[source]¶
Filter: Select the p-values corresponding to Family-wise error rate.
Read more in the User Guide.
- Parameters:
- score_funccallable, default=f_classif
Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues). Default is f_classif (see below “See Also”). The default function only works with classification tasks.
- alphafloat, default=5e-2
The highest uncorrected p-value for features to keep.
- Attributes:
- scores_array-like of shape (n_features,)
Scores of features.
- pvalues_array-like of shape (n_features,)
p-values of feature scores.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
See also
f_classif
ANOVA F-value between label/feature for classification tasks.
chi2
Chi-squared stats of non-negative features for classification tasks.
f_regression
F-value between label/feature for regression tasks.
SelectPercentile
Select features based on percentile of the highest scores.
SelectKBest
Select features based on the k highest scores.
SelectFpr
Select features based on a false positive rate test.
SelectFdr
Select features based on an estimated false discovery rate.
GenericUnivariateSelect
Univariate feature selector with configurable mode.
Examples
>>> from sklearn.datasets import load_breast_cancer >>> from sklearn.feature_selection import SelectFwe, chi2 >>> X, y = load_breast_cancer(return_X_y=True) >>> X.shape (569, 30) >>> X_new = SelectFwe(chi2, alpha=0.01).fit_transform(X, y) >>> X_new.shape (569, 15)
Methods
fit
(X, y)Run score function on (X, y) and get the appropriate features.
fit_transform
(X[, y])Fit to data, then transform it.
get_feature_names_out
([input_features])Mask feature names according to selected features.
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)[source]¶
Run score function on (X, y) and get the appropriate features.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
The training input samples.
- yarray-like of shape (n_samples,)
The target values (class labels in classification, real numbers in regression).
- Returns:
- selfobject
Returns the instance itself.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- 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_feature_names_out(input_features=None)[source]¶
Mask feature names according to selected features.
- Parameters:
- input_featuresarray-like of str or None, default=None
Input features.
If
input_features
isNone
, thenfeature_names_in_
is used as feature names in. Iffeature_names_in_
is not defined, then the following input feature names are generated:["x0", "x1", ..., "x(n_features_in_ - 1)"]
.If
input_features
is an array-like, theninput_features
must matchfeature_names_in_
iffeature_names_in_
is defined.
- Returns:
- feature_names_outndarray of str objects
Transformed feature names.
- 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. 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:
- 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 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
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.