Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/12702#discussion_r61107574
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala
---
@@ -31,12 +31,9 @@ import org.apache.spark.sql.types.{ArrayType, StructType}
/**
* :: Experimental ::
* Maps a sequence of terms to their term frequencies using the hashing
trick.
- * Currently we support two hash algorithms: "murmur3" (default) and
"native".
+ * Currently we support one hash algorithms "murmur3" which is also the
default option.
--- End diff --
(Somewhat related to this PR) By the way, it is usual to select a vector
size that is a power of 2, e.g. [scikit-learn doc mentions
this](http://scikit-learn.org/stable/modules/feature_extraction.html#implementation-details):
> Since a simple modulo is used to transform the hash function to a column
index, it is advisable to use a power of two as the n_features parameter;
otherwise the features will not be mapped evenly to the columns.
We should add that to the doc
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