sklearn.feature_selection.SequentialFeatureSelector¶
-
class
sklearn.feature_selection.SequentialFeatureSelector(estimator, *, n_features_to_select=None, direction='forward', scoring=None, cv=5, n_jobs=None)[source]¶ Transformer that performs Sequential Feature Selection.
This Sequential Feature Selector adds (forward selection) or removes (backward selection) features to form a feature subset in a greedy fashion. At each stage, this estimator chooses the best feature to add or remove based on the cross-validation score of an estimator.
Read more in the User Guide.
New in version 0.24.
- Parameters
- estimatorestimator instance
An unfitted estimator.
- n_features_to_selectint or float, default=None
The number of features to select. If
None, half of the features are selected. If integer, the parameter is the absolute number of features to select. If float between 0 and 1, it is the fraction of features to select.- direction{‘forward’, ‘backward’}, default=’forward’
Whether to perform forward selection or backward selection.
- scoringstr, callable, list/tuple or dict, default=None
A single str (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set.
NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each.
If None, the estimator’s score method is used.
- cvint, cross-validation generator or an iterable, default=None
Determines the cross-validation splitting strategy. Possible inputs for cv are:
None, to use the default 5-fold cross validation,
integer, to specify the number of folds in a
(Stratified)KFold,An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and
yis either binary or multiclass,StratifiedKFoldis used. In all other cases,KFoldis used. These splitters are instantiated withshuffle=Falseso the splits will be the same across calls.Refer User Guide for the various cross-validation strategies that can be used here.
- n_jobsint, default=None
Number of jobs to run in parallel. When evaluating a new feature to add or remove, the cross-validation procedure is parallel over the folds.
Nonemeans 1 unless in ajoblib.parallel_backendcontext.-1means using all processors. See Glossary for more details.
- Attributes
- n_features_to_select_int
The number of features that were selected.
- support_ndarray of shape (n_features,), dtype=bool
The mask of selected features.
See also
RFERecursive feature elimination based on importance weights.
RFECVRecursive feature elimination based on importance weights, with automatic selection of the number of features.
SelectFromModelFeature selection based on thresholds of importance weights.
Examples
>>> from sklearn.feature_selection import SequentialFeatureSelector >>> from sklearn.neighbors import KNeighborsClassifier >>> from sklearn.datasets import load_iris >>> X, y = load_iris(return_X_y=True) >>> knn = KNeighborsClassifier(n_neighbors=3) >>> sfs = SequentialFeatureSelector(knn, n_features_to_select=3) >>> sfs.fit(X, y) SequentialFeatureSelector(estimator=KNeighborsClassifier(n_neighbors=3), n_features_to_select=3) >>> sfs.get_support() array([ True, False, True, True]) >>> sfs.transform(X).shape (150, 3)
Methods
fit(X, y)Learn the features to select.
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
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]¶ Learn the features to select.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Training vectors.
- yarray-like of shape (n_samples,)
Target values.
- Returns
- selfobject
-
fit_transform(X, y=None, **fit_params)[source]¶ Fit to data, then transform it.
Fits transformer to
Xandywith optional parametersfit_paramsand 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_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
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.
-
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]
Xwith 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.
