class_docstr: Meta-transformer for selecting features based on importance weights.
  
  .. versionadded:: 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 non-fitted 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 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, ``transform`` must be called directly
      and SelectFromModel cannot be used with ``cross_val_score``,
      ``GridSearchCV`` and similar utilities that clone the estimator.
      Otherwise train the model using ``fit`` and then ``transform`` to do
      feature selection.
  
  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.
  
  `threshold_`: float
      The threshold value used for feature selection.
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docstr: Fit the SelectFromModel meta-transformer only once.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      The training input samples.
  
  y : array-like, 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.
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code: def partial_fit(self, 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, 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.
      """
      if self.prefit:
          raise NotFittedError(
              "Since 'prefit=True', call transform directly"
          )
      if not hasattr(self, "estimator_"):
          self.estimator_ = clone(self.estimator)
      self.estimator_.partial_fit(X, y, **fit_params)
      return self
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/feature_selection/from_model.py
imports: import numpy
  from .base import SelectorMixin
  from ..base import TransformerMixin, BaseEstimator, clone
  from ..externals import six
  from ..utils import safe_mask, check_array, deprecated
  from ..utils.validation import check_is_fitted
  from ..exceptions import NotFittedError
label_desc: Use image node_id 4 for calling the SelectFromModel obj's underlying member method code with example usage: obj.partial_fit(X, y) and returns: self