Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6093#discussion_r30177188
--- Diff: docs/ml-features.md ---
@@ -0,0 +1,181 @@
+---
+layout: global
+title: Feature Extraction, Transformation, and Selection - SparkML
+displayTitle: <a href="ml-guide.html">ML</a> - Features
+---
+
+This section covers algorithms for working with features, roughly divided
into these groups:
+* Extraction: Extracting features from "raw" data
+* Transformation: Scaling, converting, or modifying features
+* Selection: Selecting a subset from a larger set of features
+
+**Table of Contents**
+
+* This will become a table of contents (this text will be scraped).
+{:toc}
+
+
+# Feature Extractors
+
+## Hashing Term-Frequency (HashingTF)
+
+`HashingTF` is a `Transformer` which takes sets of terms (e.g., `String`
terms can be sets of words) and converts those sets into fixed-length feature
vectors.
+The algorithm combines [Term Frequency
(TF)](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) counts with the [hashing
trick](http://en.wikipedia.org/wiki/Feature_hashing) for dimensionality
reduction. Please refer to the [MLlib user guide on
TF-IDF](mllib-feature-extraction.html#tf-idf) for more details on
Term-Frequency.
+
+HashingTF is implemented in
+[HashingTF](api/scala/index.html#org.apache.spark.ml.feature.HashingTF).
+In the following code segment, we start with a set of sentences. We split
each sentence into words using `Tokenizer`. For each sentence (bag of words),
we hash it into a feature vector. This feature vector could then be passed to
a learning algorithm.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
+case class LabeledSentence(label: Double, sentence: String)
--- End diff --
It would be easier to read if we follow the same Spark code style in
example code. Should have empty lines before and after `case class ...`. The
case class is not necessary to create DataFrames.
~~~scala
val sentences = sqlContext.createDataFrame(Seq(
(0, "Hi ..."),
...
)).toDF("label", "sentence")
~~~
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