Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6501#discussion_r31376865
--- Diff: docs/ml-features.md ---
@@ -876,5 +876,95 @@ bucketedData = bucketizer.transform(dataFrame)
</div>
</div>
+## ElementwiseProduct
+
+ElementwiseProduct multiplies each input vector by a provided "weight"
vector, using element-wise multiplication. In other words, it scales each
column of the dataset by a scalar multiplier. This represents the [Hadamard
product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29) between
the input vector, `v` and transforming vector, `w`, to yield a result vector.
+
+`\[ \begin{pmatrix}
+v_1 \\
+\vdots \\
+v_N
+\end{pmatrix} \circ \begin{pmatrix}
+ w_1 \\
+ \vdots \\
+ w_N
+ \end{pmatrix}
+= \begin{pmatrix}
+ v_1 w_1 \\
+ \vdots \\
+ v_N w_N
+ \end{pmatrix}
+\]`
+
+[`ElementwiseProduct`](api/scala/index.html#org.apache.spark.ml.feature.ElementwiseProduct)
takes the following parameter:
+
+* `scalingVec`: the transforming vector.
+
+This example below demonstrates how to transform vectors using a
transforming vector value.
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.SparkContext._
--- End diff --
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