sklearn.feature_selection
.SelectFromModel¶

class
sklearn.feature_selection.
SelectFromModel
(estimator, threshold=None, prefit=False)[source]¶ Metatransformer for selecting features based on importance weights.
New in version 0.17.
Parameters: estimator : object
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.threshold : string, 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 implicity (e.g, Lasso), the threshold is used is 1e5. Otherwise, “mean” is used by default.prefit : bool, 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.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.
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 inverse_transform
(X)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. 
fit
(X, y=None, **fit_params)[source]¶ Fit the SelectFromModel metatransformer.
Parameters: X : arraylike of shape (n_samples, n_features)
The training input samples.
y : arraylike, shape (n_samples,)
The target values (integers that correspond to classes in classification, real numbers in regression).
**fit_params : Other estimator specific parameters
Returns: self : object
Returns self.

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 : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns: X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping 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: indices : boolean (default False)
If True, the return value will be an array of integers, rather than a boolean mask.
Returns: support : array
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.

inverse_transform
(X)[source]¶ Reverse the transformation operation
Parameters: X : array of shape [n_samples, n_selected_features]
The input samples.
Returns: X_r : array of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform.

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

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 former have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: self :
