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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