Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/20257#discussion_r161473460
--- Diff: docs/ml-features.md ---
@@ -807,6 +809,36 @@ for more details on the API.
</div>
</div>
+## OneHotEncoderEstimator
+
+[One-hot encoding](http://en.wikipedia.org/wiki/One-hot) maps a column of
label indices to a column of binary vectors, with at most a single one-value.
This encoding allows algorithms which expect continuous features, such as
Logistic Regression, to use categorical features.
--- End diff --
We should add a note that it can handle multiple columns (and returns a
one-hot-encoded output vector column for _each_ input column, rather than
merging into one output vector).
Also, what about describing the missing / invalid value handling in more
detail?
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