Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6126#discussion_r30287964
--- Diff: docs/ml-features.md ---
@@ -183,6 +183,75 @@ for words_label in wordsDataFrame.select("words",
"label").take(3):
</div>
</div>
+## OneHotEncoder
+
+One-hot encoding is a way of formatting categorical features as input into
machine learning algorithms. It maps a column of label indices to a column of
binary vectors, with at most a single one-value. The
[OneHotEncoder](api/scala/index.html#org.apache.spark.ml.feature.OneHotEncoder)
class provides this functionality. By default, the resulting binary vector has
a component for each category, so with 5 categories, an input value of 2.0
would map to an output vector of (0.0, 0.0, 1.0, 0.0, 0.0). If the includeFirst
is set to false, the first category is omitted, so the output vector for the
previous example would be (0.0, 1.0, 0.0, 0.0) and an input value of 0.0 would
map to a vector of all zeros. Including the first category makes the vector
columns linearly dependent because they sum up to one.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.feature.{OneHotEncoder, StringIndexer}
+
+val df = sqlContext.createDataFrame(Seq(
+ (0, "a"),
+ (1, "b"),
+ (2, "c"),
+ (3, "a"),
+ (4, "a"),
+ (5, "c")
+)).toDF("id", "category")
+
+val indexer = new StringIndexer().setInputCol("category").
--- End diff --
That's a good point about dots on the line above. I'm not sure, but I'll
ask & let you know.
I'll buy that about StringIndexer, so let's keep it.
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