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# `sklearn.feature_selection`.SelectFromModel

class sklearn.feature_selection.SelectFromModel(estimator, *, threshold=None, prefit=False, norm_order=1, max_features=None, importance_getter='auto')

  Meta-transformer for selecting features based on importance weights.

  New in version 0.17.

  Read more in the User Guide.

  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 non-fitted estimator. The estimator should have a `feature_importances_` or `coef_` attribute after fitting. Otherwise, the `importance_getter` parameter should be used.

    threshold str or float, default=None

      The threshold value to use for feature selection. Features whose absolute importance value 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.

    prefit bool, default=False

      Whether a prefit model is expected to be passed into the constructor directly or not. If `True`, `estimator` must be a fitted estimator. If `False`, `estimator` is fitted and updated by calling `fit` and `partial_fit`, respectively.

    norm_order non-zero int, inf, -inf, default=1

      Order of the norm used to filter the vectors of coefficients below `threshold` in the case where the `coef_` attribute of the estimator is of dimension 2.

    max_features int, callable, default=None

      The maximum number of features to select.

      - If an integer, then it specifies the maximum number of features to allow.

      - If a callable, then it specifies how to calculate the maximum number of features allowed by using the output of `max_features(X)`.

      - If `None`, then all features are kept.

      To only select based on `max_features`, set `threshold=-np.inf`.

      New in version 0.20.

      Changed in version 1.1: `max_features` accepts a callable.

    importance_getter str or callable, default=’auto’

      If ‘auto’, uses the feature importance either through a `coef_` attribute or `feature_importances_` attribute of estimator.

      Also accepts a string that specifies an attribute name/path for extracting feature importance (implemented with `attrgetter`). For example, give `regressor_.coef_` in case of `TransformedTargetRegressor` or `named_steps.clf.feature_importances_` in case of `Pipeline` with its last step named `clf`.

      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_estimator

      The base estimator from which the transformer is built. This attribute exist only when `fit` has been called.

      - If `prefit=True`, it is a deep copy of `estimator`.

      - If `prefit=False`, it is a clone of `estimator` and fit on the data passed to `fit` or `partial_fit`.

    `n_features_in_`int

      Number of features seen during `fit`.

    max_features_int

      Maximum number of features calculated during fit. Only defined if the `max_features` is not `None`.

      - If `max_features` is an `int`, then `max_features_ = max_features`.

      - If `max_features` is a callable, then `max_features_ = max_features(X)`.

      New in version 1.1.

    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_`float

      Threshold 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]])

  Using a callable to create a selector that can use no more than half of the input features.

      >>> def half_callable(X):
      ...     return round(len(X[0]) / 2)
      >>> half_selector = SelectFromModel(estimator=LogisticRegression(),
      ...                                 max_features=half_callable)
      >>> _ = half_selector.fit(X, y)
      >>> half_selector.max_features_
      2

  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_metadata_routing`() | Get metadata routing of this object.
  `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 meta-transformer only once.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `transform`(X) | Reduce X to the selected features.

  fit(X, y=None, **fit_params)

    Fit the SelectFromModel meta-transformer.

    Parameters:
      X array-like of shape (n_samples, n_features)

        The training input samples.

      y array-like of shape (n_samples,), default=None

        The target values (integers that correspond to classes in classification, real numbers in regression).

      **fit_params dict

        Other estimator specific parameters.

    Returns:
      self object

        Fitted estimator.

  fit_transform(X, y=None, **fit_params)

    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 array-like of shape (n_samples, n_features)

        Input samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs), default=None

        Target values (None for unsupervised transformations).

      **fit_params dict

        Additional fit parameters.

    Returns:
      X_new ndarray array of shape (n_samples, n_features_new)

        Transformed array.

  get_feature_names_out(input_features=None)

    Mask feature names according to selected features.

    Parameters:
      input_features array-like of str or None, default=None

        Input features.

        - If `input_features` is `None`, then `feature_names_in_` is used as feature names in. If `feature_names_in_` is not defined, then the following input feature names are generated: `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.

        - If `input_features` is an array-like, then `input_features` must match `feature_names_in_` if `feature_names_in_` is defined.

    Returns:
      feature_names_out ndarray of str objects

        Transformed feature names.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

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

    Returns:
      params dict

        Parameter names mapped to their values.

  get_support(indices=False)

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

    Parameters:
      indices bool, 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)

    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`.

  property n_features_in_

    Number of features seen during `fit`.

  partial_fit(X, y=None, **fit_params)

    Fit the SelectFromModel meta-transformer only once.

    Parameters:
      X array-like of shape (n_samples, n_features)

        The training input samples.

      y array-like of shape (n_samples,), default=None

        The target values (integers that correspond to classes in classification, real numbers in regression).

      **fit_params dict

        Other estimator specific parameters.

    Returns:
      self object

        Fitted estimator.

  set_output(*, transform=None)

    Set output container.

    See Introducing the set_output API for an example on how to use the API.

    Parameters:
      transform{“default”, “pandas”}, default=None

        Configure output of `transform` and `fit_transform`.

        - `"default"`: Default output format of a transformer

        - `"pandas"`: DataFrame output

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  property threshold_

    Threshold value used for feature selection.

  transform(X)

    Reduce X to the selected features.

    Parameters:
      X array of shape [n_samples, n_features]

        The input samples.

    Returns:
      X_r array of shape [n_samples, n_selected_features]

        The input samples with only the selected features.

## Examples using `sklearn.feature_selection.SelectFromModel`

Model-based and sequential feature selection

Model-based and sequential feature selection