Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6113#discussion_r30280922
--- Diff: docs/ml-features.md ---
@@ -183,6 +183,78 @@ for words_label in wordsDataFrame.select("words",
"label").take(3):
</div>
</div>
+## PolynomialExpansion
+
+[Polynomial expansion](http://en.wikipedia.org/wiki/Polynomial_expansion)
is the process of expanding your features into a polynomial space, which is
formulated by an n-degree combination of original dimensions. A
[PolynomialExpansion](api/scala/index.html#org.apache.spark.ml.feature.PolynomialExpansion)
class provides this functionality. The example below shows how to expand your
features into a 3-degree polynomial space.
+
+Note: We do not provide Python API for `PolynomialExpansion` in this
version.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.feature.PolynomialExpansion
+
+val data = Array(
+ Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))),
+ Vectors.dense(-2.0, 2.3),
--- End diff --
Length should match other data. Also, can the data be made as simple as
possible, such as 3 length-2 vectors? (same for Java example)
Alternatively, you could load data via MLUtils.loadLibSVMFile
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