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From "Bjoern Toldbod (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-18678) Skewed feature subsampling in Random forest
Date Thu, 01 Dec 2016 20:48:59 GMT
Bjoern Toldbod created SPARK-18678:
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             Summary: Skewed feature subsampling in Random forest
                 Key: SPARK-18678
                 URL: https://issues.apache.org/jira/browse/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 
org.apache.spark.ml.tree.impl.RandomForest
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 0.
Of course this produces low quality models for few features when using subsampling.



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