SelectPercentile#
- class sklearn.feature_selection.SelectPercentile(score_func=<function f_classif>, *, percentile=10)[source]#
Select features according to a percentile of the highest scores.
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) or a single array with scores. Default is f_classif (see below “See Also”). The default function only works with classification tasks.
Added in version 0.18.
- percentileint, default=10
Percent of 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, None if
score_func
returned only scores.- n_features_in_int
Number of features seen during fit.
Added 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.Added in version 1.0.
See also
f_classif
ANOVA f-value between label/feature for classification tasks.
mutual_info_classif
Mutual information for a discrete target.
chi2
Chi-squared stats of non-negative features for classification tasks.
f_regression
f-value between label/feature for regression tasks.
mutual_info_regression
Mutual information for a continuous target.
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.
Selectfwe
Select features based on family-wise error rate.
GenericUnivariateSelect
Univariate feature selector with configurable mode.
Notes
Ties between features with equal scores will be broken in an unspecified way.
This filter supports unsupervised feature selection that only requests
X
for computing the scores.Examples
>>> from sklearn.datasets import load_digits >>> from sklearn.feature_selection import SelectPercentile, chi2 >>> X, y = load_digits(return_X_y=True) >>> X.shape (1797, 64) >>> X_new = SelectPercentile(chi2, percentile=10).fit_transform(X, y) >>> X_new.shape (1797, 7)
- fit(X, y=None)[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,) or None
The target values (class labels in classification, real numbers in regression). If the selector is unsupervised then
y
can be set toNone
.
- 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_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- 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_output(*, transform=None)[source]#
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”, “polars”}, default=None
Configure output of
transform
andfit_transform
."default"
: Default output format of a transformer"pandas"
: Dataframe output"polars"
: Polars outputNone
: Transform configuration is unchanged
Added in version 1.4:
"polars"
option was added.
- Returns:
- selfestimator instance
Estimator instance.
- 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.
Gallery examples#
feature agglomeration vs. univariate selection
Introducing the set_output API
Column Transformer with Mixed Types
SVM-Anova: SVM with univariate feature selection