sklearn.feature_selection
.SelectFromModel¶
- class sklearn.feature_selection.SelectFromModel(estimator, *, threshold=None, prefit=False, norm_order=1, max_features=None, importance_getter='auto')[source]¶
Meta-transformer for selecting features based on importance weights.
New in version 0.17.
Read more in the User Guide.
- Parameters
- estimatorobject
The base estimator from which the transformer is built. This can be both a fitted (if
prefit
is set to True) or a non-fitted estimator. The estimator should have afeature_importances_
orcoef_
attribute after fitting. Otherwise, theimportance_getter
parameter should be used.- thresholdstr or float, default=None
The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If “median” (resp. “mean”), then the
threshold
value is the median (resp. the mean) of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. If None and if the estimator has a parameter penalty set to l1, either explicitly or implicitly (e.g, Lasso), the threshold used is 1e-5. Otherwise, “mean” is used by default.- prefitbool, default=False
Whether a prefit model is expected to be passed into the constructor directly or not. If True,
transform
must be called directly and SelectFromModel cannot be used withcross_val_score
,GridSearchCV
and similar utilities that clone the estimator. Otherwise train the model usingfit
and thentransform
to do feature selection.- norm_ordernon-zero int, inf, -inf, default=1
Order of the norm used to filter the vectors of coefficients below
threshold
in the case where thecoef_
attribute of the estimator is of dimension 2.- max_featuresint, default=None
The maximum number of features to select. To only select based on
max_features
, setthreshold=-np.inf
.New in version 0.20.
- importance_getterstr or callable, default=’auto’
If ‘auto’, uses the feature importance either through a
coef_
attribute orfeature_importances_
attribute of estimator.Also accepts a string that specifies an attribute name/path for extracting feature importance (implemented with
attrgetter
). For example, giveregressor_.coef_
in case ofTransformedTargetRegressor
ornamed_steps.clf.feature_importances_
in case ofPipeline
with its last step namedclf
.If
callable
, overrides the default feature importance getter. The callable is passed with the fitted estimator and it should return importance for each feature.New in version 0.24.
- Attributes
- estimator_an estimator
The base estimator from which the transformer is built. This is stored only when a non-fitted estimator is passed to the
SelectFromModel
, i.e when prefit is False.n_features_in_
intNumber of features seen during
fit
.- 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.
threshold_
floatThreshold value used for feature selection.
See also
RFE
Recursive feature elimination based on importance weights.
RFECV
Recursive feature elimination with built-in cross-validated selection of the best number of features.
SequentialFeatureSelector
Sequential cross-validation based feature selection. Does not rely on importance weights.
Notes
Allows NaN/Inf in the input if the underlying estimator does as well.
Examples
>>> from sklearn.feature_selection import SelectFromModel >>> from sklearn.linear_model import LogisticRegression >>> X = [[ 0.87, -1.34, 0.31 ], ... [-2.79, -0.02, -0.85 ], ... [-1.34, -0.48, -2.55 ], ... [ 1.92, 1.48, 0.65 ]] >>> y = [0, 1, 0, 1] >>> selector = SelectFromModel(estimator=LogisticRegression()).fit(X, y) >>> selector.estimator_.coef_ array([[-0.3252302 , 0.83462377, 0.49750423]]) >>> selector.threshold_ 0.55245... >>> selector.get_support() array([False, True, False]) >>> selector.transform(X) array([[-1.34], [-0.02], [-0.48], [ 1.48]])
Methods
fit
(X[, y])Fit the SelectFromModel meta-transformer.
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.
partial_fit
(X[, y])Fit the SelectFromModel meta-transformer only once.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Reduce X to the selected features.
- fit(X, y=None, **fit_params)[source]¶
Fit the SelectFromModel meta-transformer.
- Parameters
- Xarray-like of shape (n_samples, n_features)
The training input samples.
- yarray-like of shape (n_samples,), default=None
The target values (integers that correspond to classes in classification, real numbers in regression).
- **fit_paramsdict
Other estimator specific parameters.
- Returns
- selfobject
Fitted estimator.
- 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 names are generated:[x0, x1, ..., x(n_features_in_)]
.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
.
- property n_features_in_¶
Number of features seen during
fit
.
- partial_fit(X, y=None, **fit_params)[source]¶
Fit the SelectFromModel meta-transformer only once.
- Parameters
- Xarray-like of shape (n_samples, n_features)
The training input samples.
- yarray-like of shape (n_samples,), default=None
The target values (integers that correspond to classes in classification, real numbers in regression).
- **fit_paramsdict
Other estimator specific parameters.
- Returns
- selfobject
Fitted estimator.
- 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.
- property threshold_¶
Threshold value used for feature selection.