@property
@since('2.0.0')
def featureImportances(self):
    '\n        Estimate of the importance of each feature.\n\n        This generalizes the idea of "Gini" importance to other losses,\n        following the explanation of Gini importance from "Random Forests" documentation\n        by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.\n\n        This feature importance is calculated as follows:\n          - importance(feature j) = sum (over nodes which split on feature j) of the gain,\n            where gain is scaled by the number of instances passing through node\n          - Normalize importances for tree to sum to 1.\n\n        .. note:: Feature importance for single decision trees can have high variance due to\n              correlated predictor variables. Consider using a :py:class:`RandomForestRegressor`\n              to determine feature importance instead.\n        '
    return self._call_java('featureImportances')
Estimate of the importance of each feature.

This generalizes the idea of "Gini" importance to other losses,
following the explanation of Gini importance from "Random Forests" documentation
by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.

This feature importance is calculated as follows:
  - importance(feature j) = sum (over nodes which split on feature j) of the gain,
    where gain is scaled by the number of instances passing through node
  - Normalize importances for tree to sum to 1.

.. note:: Feature importance for single decision trees can have high variance due to
      correlated predictor variables. Consider using a :py:class:`RandomForestRegressor`
      to determine feature importance instead.
@property
@since('2.0.0')
def featureImportances(self):
    '\n        Estimate of the importance of each feature.\n\n        This generalizes the idea of "Gini" importance to other losses,\n        following the explanation of Gini importance from "Random Forests" documentation\n        by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.\n\n        This feature importance is calculated as follows:\n          - importance(feature j) = sum (over nodes which split on feature j) of the gain,\n            where gain is scaled by the number of instances passing through node\n          - Normalize importances for tree to sum to 1.\n\n        .. note:: Feature importance for single decision trees can have high variance due to\n              correlated predictor variables. Consider using a :py:class:`RandomForestRegressor`\n              to determine feature importance instead.\n        '
    return self._call_java('featureImportances')