code: public <Type extends DataSet> OnLineStatistics[] evaluateFeatureImportance(DataSet<Type> data)
      {
          if(data instanceof ClassificationDataSet)
              return evaluateFeatureImportance(data, new MDI(ImpurityScore.ImpurityMeasure.GINI));
          else
              return evaluateFeatureImportance(data, new ImportanceByUses());
      }
docs: Measures the statistics of feature importance from the trees in this
  forest. For classification datasets, the {@link MDI} method with Gini
  impurity will be used. For others, the {@link ImportanceByUses} method
  will be used. This may change in the future.
  
  @param <Type>
  @param data the dataset to infer the feature importance from with respect
  to the current model.
  @return an array of statistics, which each index corresponds to a
  specific feature. Numeric features start from the zero index, categorical
  features start from the index equal to the number of numeric features.