class_docstr: Implements common methods among tree models:
  - BoostedTreesClassifier
  - BoostedTreesRegression
  - RandomForestClassifier
  - RandomForestRegression
  - DecisionTreeClassifier
  - DecisionTreeRegression
class_docstr_tok: ["Implements","common","methods","among","tree","models",":","-","BoostedTreesClassifier","-","BoostedTreesRegression","-","RandomForestClassifier","-","RandomForestRegression","-","DecisionTreeClassifier","-","DecisionTreeRegression"]
docstr: Get the importance of features used by the model.
  
  The measure of importance of feature X
  is determined by the sum of occurrence of X
  as a branching node in all trees.
  
  When X is a categorical feature, e.g. "Gender",
  the index column contains the value of the feature, e.g. "M" or "F".
  When X is a numerical feature, index of X is None.
  
  Returns
  -------
  out : SFrame
      A table with three columns: name, index, count,
      ordered by 'count' in descending order.
  
  Examples
  --------
  >>> m.get_feature_importance()
  Rows: 31
  Data:
      +-----------------------------+-------+-------+
      |             name            | index | count |
      +-----------------------------+-------+-------+
      | DER_mass_transverse_met_lep |  None |   66  |
      |         DER_mass_vis        |  None |   65  |
      |          PRI_tau_pt         |  None |   61  |
      |         DER_mass_MMC        |  None |   59  |
      |      DER_deltar_tau_lep     |  None |   58  |
      |          DER_pt_tot         |  None |   41  |
      |           PRI_met           |  None |   38  |
      |     PRI_jet_leading_eta     |  None |   30  |
      |     DER_deltaeta_jet_jet    |  None |   27  |
      |       DER_mass_jet_jet      |  None |   24  |
      +-----------------------------+-------+-------+
      [31 rows x 3 columns]
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code: def get_feature_importance(self):
      """
      Get the importance of features used by the model.
  
      The measure of importance of feature X
      is determined by the sum of occurrence of X
      as a branching node in all trees.
  
      When X is a categorical feature, e.g. "Gender",
      the index column contains the value of the feature, e.g. "M" or "F".
      When X is a numerical feature, index of X is None.
  
      Returns
      -------
      out : SFrame
          A table with three columns: name, index, count,
          ordered by 'count' in descending order.
  
      Examples
      --------
      >>> m.get_feature_importance()
      Rows: 31
      Data:
          +-----------------------------+-------+-------+
          |             name            | index | count |
          +-----------------------------+-------+-------+
          | DER_mass_transverse_met_lep |  None |   66  |
          |         DER_mass_vis        |  None |   65  |
          |          PRI_tau_pt         |  None |   61  |
          |         DER_mass_MMC        |  None |   59  |
          |      DER_deltar_tau_lep     |  None |   58  |
          |          DER_pt_tot         |  None |   41  |
          |           PRI_met           |  None |   38  |
          |     PRI_jet_leading_eta     |  None |   30  |
          |     DER_deltaeta_jet_jet    |  None |   27  |
          |       DER_mass_jet_jet      |  None |   24  |
          +-----------------------------+-------+-------+
          [31 rows x 3 columns]
      """
      return tc.extensions._xgboost_feature_importance(self.__proxy__)
  
file_path: turicreate/src/python/turicreate/toolkits/_tree_model_mixin.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe
  from turicreate.toolkits._supervised_learning import select_default_missing_value_policy
  from turicreate.toolkits import _coreml_utils
label_desc: Use image node_id 1 for calling the TreeModelMixin obj's underlying member method code with example usage: obj.get_feature_importance() and returns: tc