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.

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,

  • CV splitter,

  • An iterable yielding (train, test) splits as arrays of indices.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

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. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.


The number of features that were selected.

support_ndarray of shape (n_features,), dtype=bool

The mask of selected features.

See also


Recursive feature elimination based on importance weights.


Recursive feature elimination based on importance weights, with automatic selection of the number of features.


Feature selection based on thresholds of importance weights.


>>> 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)
>>>, y)
>>> sfs.get_support()
array([ True, False,  True,  True])
>>> sfs.transform(X).shape
(150, 3)


fit(X, y)

Learn the features to select.

fit_transform(X[, y])

Fit to data, then transform it.


Get parameters for this estimator.


Get a mask, or integer index, of the features selected


Reverse the transformation operation


Set the parameters of this estimator.


Reduce X to the selected features.

fit(X, y)[source]

Learn the features to select.

Xarray-like of shape (n_samples, n_features)

Training vectors.

yarray-like of shape (n_samples,)

Target values.

fit_transform(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.

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).


Additional fit parameters.

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.


Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.


Parameter names mapped to their values.


Get a mask, or integer index, of the features selected

indicesbool, default=False

If True, the return value will be an array of integers, rather than a boolean mask.


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

Xarray of shape [n_samples, n_selected_features]

The input samples.

X_rarray of shape [n_samples, n_original_features]

X with columns of zeros inserted where features would have been removed by transform.


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.


Estimator parameters.

selfestimator instance

Estimator instance.


Reduce X to the selected features.

Xarray of shape [n_samples, n_features]

The input samples.

X_rarray of shape [n_samples, n_selected_features]

The input samples with only the selected features.

Examples using sklearn.feature_selection.SequentialFeatureSelector