SelectPercentile (original) (raw)
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 Xhas feature names that are all strings.
Added in version 1.0.
See also
ANOVA F-value between label/feature for classification tasks.
Mutual information for a discrete target.
Chi-squared stats of non-negative features for classification tasks.
F-value between label/feature for regression tasks.
Mutual information for a continuous target.
Select features based on the k highest scores.
Select features based on a false positive rate test.
Select features based on an estimated false discovery rate.
Select features based on family-wise error rate.
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)
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 to None.
Returns:
selfobject
Returns the instance itself.
fit_transform(X, y=None, **fit_params)[source]#
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_paramsand 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_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_featuresisNone, 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_featuresis an array-like, theninput_featuresmust 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. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.
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_output(*, transform=None)[source]#
Set output container.
See Introducing the set_output APIfor an example on how to use the API.
Parameters:
transform{“default”, “pandas”, “polars”}, default=None
Configure output of transform and fit_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 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.
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.