@property
@since('2.0.0')
def featureImportances(self):
    '\n        Estimate of the importance of each feature.\n\n        Each feature\'s importance is the average of its importance across all trees in the ensemble\n        The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.\n        (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)\n        and follows the implementation from scikit-learn.\n\n        .. seealso:: :py:attr:`DecisionTreeRegressionModel.featureImportances`\n        '
    return self._call_java('featureImportances')
Estimate of the importance of each feature.

Each feature's importance is the average of its importance across all trees in the ensemble
The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.
(Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)
and follows the implementation from scikit-learn.

.. seealso:: :py:attr:`DecisionTreeRegressionModel.featureImportances`
@property
@since('2.0.0')
def featureImportances(self):
    '\n        Estimate of the importance of each feature.\n\n        Each feature\'s importance is the average of its importance across all trees in the ensemble\n        The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.\n        (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)\n        and follows the implementation from scikit-learn.\n\n        .. seealso:: :py:attr:`DecisionTreeRegressionModel.featureImportances`\n        '
    return self._call_java('featureImportances')