Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/20257#discussion_r161854406
--- Diff: docs/ml-features.md ---
@@ -775,35 +775,43 @@ for more details on the API.
</div>
</div>
-## OneHotEncoder
+## OneHotEncoder (Deprecated since 2.3.0)
-[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.
+Because this existing `OneHotEncoder` is a stateless transformer, it is
not usable on new data where the number of categories may differ from the
training data. In order to fix this, a new `OneHotEncoderEstimator` was created
that produces an `OneHotEncoderModel` when fitting. For more detail, please see
the JIRA ticket (https://issues.apache.org/jira/browse/SPARK-13030).
+
+`OneHotEncoder` has been deprecated in 2.3.0 and will be removed in 3.0.0.
Please use [OneHotEncoderEstimator](ml-features.html#onehotencoderestimator)
for one-hot encoding instead.
+
+## 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. For string type input data,
it is common to encode categorical features using
[StringIndexer](ml-features.html#stringindexer) first.
+
+`OneHotEncoderEstimator` can handle multi-column. By specifying multiple
input columns, it returns a one-hot-encoded output vector column for each input
column.
+
+`OneHotEncoderEstimator` supports `handleInvalid` parameter to choose how
to handle invalid data during transforming data. Available options include
'keep' (invalid data presented as an extra categorical feature) and 'error'
(throw an error).
--- End diff --
"(... to an extra categorical number)"
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