Bjoern Toldbod created SPARK-18678:

             Summary: Skewed feature subsampling in Random forest
                 Key: SPARK-18678
             Project: Spark
          Issue Type: Bug
          Components: ML
    Affects Versions: 2.0.2
            Reporter: Bjoern Toldbod

The feature subsampling performed in the RandomForest-implementation from
is performed using SamplingUtils.reservoirSampleAndCount

The implementation of the sampling skews feature selection in favor of features 
with a higher index. 
The skewness is smaller for a large number of features, but completely 
dominates the feature selection for a small number of features. The extreme 
case is when the number of features is 2 and number of features to select is 1.

In this case the feature sampling will always pick feature 1 and ignore feature 
Of course this produces low quality models for few features when using 

This message was sent by Atlassian JIRA

To unsubscribe, e-mail:
For additional commands, e-mail:

Reply via email to