sklearn.feature_selection
.SelectFromModel¶

class
sklearn.feature_selection.
SelectFromModel
(estimator, *, threshold=None, prefit=False, norm_order=1, max_features=None)[source]¶ Metatransformer for selecting features based on importance weights.
New in version 0.17.
 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 nonfitted estimator. The estimator must have either afeature_importances_
orcoef_
attribute after fitting. thresholdstring, float, optional 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 1e5. 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_ordernonzero 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 or None, optional
The maximum number of features to select. To only select based on
max_features
, setthreshold=np.inf
.New in version 0.20.
 Attributes
 estimator_an estimator
The base estimator from which the transformer is built. This is stored only when a nonfitted estimator is passed to the
SelectFromModel
, i.e when prefit is False. threshold_float
The threshold value used for feature selection.
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 metatransformer.
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
partial_fit
(X[, y])Fit the SelectFromModel metatransformer only once.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Reduce X to the selected features.

__init__
(estimator, *, threshold=None, prefit=False, norm_order=1, max_features=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(X, y=None, **fit_params)[source]¶ Fit the SelectFromModel metatransformer.
 Parameters
 Xarraylike of shape (n_samples, n_features)
The training input samples.
 yarraylike, shape (n_samples,)
The target values (integers that correspond to classes in classification, real numbers in regression).
 **fit_paramsOther estimator specific parameters
 Returns
 selfobject

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.
 Parameters
 X{arraylike, sparse matrix, dataframe} of shape (n_samples, n_features)
 yndarray of shape (n_samples,), default=None
Target values.
 **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
 paramsmapping of string to any
Parameter names mapped to their values.

get_support
(indices=False)[source]¶ Get a mask, or integer index, of the features selected
 Parameters
 indicesboolean (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
.

partial_fit
(X, y=None, **fit_params)[source]¶ Fit the SelectFromModel metatransformer only once.
 Parameters
 Xarraylike of shape (n_samples, n_features)
The training input samples.
 yarraylike, shape (n_samples,)
The target values (integers that correspond to classes in classification, real numbers in regression).
 **fit_paramsOther estimator specific parameters
 Returns
 selfobject

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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
 selfobject
Estimator instance.