Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/12454#discussion_r60013175
--- Diff: docs/ml-features.md ---
@@ -22,10 +22,19 @@ This section covers algorithms for working with
features, roughly divided into t
[Term Frequency-Inverse Document Frequency
(TF-IDF)](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) is a common text
pre-processing step. In Spark ML, TF-IDF is separate into two parts: TF
(+hashing) and IDF.
-**TF**: `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 [hashing
trick](http://en.wikipedia.org/wiki/Feature_hashing) for dimensionality
reduction.
+**TF**: Both `HashingTF` and `CountVectorizer` can be used to get the term
frequency.
-**IDF**: `IDF` is an `Estimator` which fits on a dataset and produces an
`IDFModel`. The `IDFModel` takes feature vectors (generally created from
`HashingTF`) and scales each column. Intuitively, it down-weights columns
which appear frequently in a corpus.
+`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
+[hashing trick](http://en.wikipedia.org/wiki/Feature_hashing) for
dimensionality reduction.
+
+`CountVectorizer` converts text documents to vectors of token counts.
Refer to [CountVectorizer
--- End diff --
we refer to `token counts` here but `term counts` elsewhere in this
section. They're interchangeable but let's stick to `term counts` throughout.
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