Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7522#discussion_r36045309
--- Diff: docs/ml-features.md ---
@@ -461,6 +461,87 @@ for binarized_feature, in binarizedFeatures.collect():
</div>
</div>
+## PCA
+
+[PCA](http://en.wikipedia.org/wiki/Principal_component_analysis) is a
statistical procedure that uses an orthogonal transformation to convert a set
of observations of possibly correlated variables into a set of values of
linearly uncorrelated variables called principal components. A
[PCA](api/scala/index.html#org.apache.spark.ml.feature.PCA) class trains a
model to project vectors to a low-dimensional space using PCA. The example
below shows how to project 5-dimensional feature vectors into 3-dimensional
principal components.
--- End diff --
Could you please include the link to the Scala API doc within the Scala
codetab here:
```
<div data-lang="scala" markdown="1">
See the [Scala API
documentation](api/scala/index.html#org.apache.spark.ml.feature.PCA) for API
details.
{% highlight scala %}
```
And please do the same for the Java and Python API docs too.
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