Major Description
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.
