Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/13176#discussion_r64077367
--- Diff: docs/ml-features.md ---
@@ -26,7 +26,9 @@ This section covers algorithms for working with features,
roughly divided into t
`HashingTF` is a `Transformer` which takes sets of terms and converts
those sets into
fixed-length feature vectors. In text processing, a "set of terms" might
be a bag of words.
-The algorithm combines Term Frequency (TF) counts with the
+A binary toggle parameter controls term frequency. When set to true all
nonzero frequencies are
+set to 1. This is especially useful for discrete probabilistic models that
model binary counts
+rather than integer. The algorithm combines Term Frequency (TF) counts
with the
[hashing trick](http://en.wikipedia.org/wiki/Feature_hashing) for
dimensionality reduction.
--- End diff --
It might better to switch the order of these sentences, so you describe the
algorithm first, then the optional binary parameter.
```
The algorithm combines...
A binary toggle parameter...
```
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