Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/20257#discussion_r161989945
--- Diff: docs/ml-features.md ---
@@ -777,17 +777,17 @@ for more details on the API.
## OneHotEncoder (Deprecated since 2.3.0)
-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).
+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
[SPARK-13030](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.
+`OneHotEncoder` has been deprecated in 2.3.0 and will be removed in 3.0.0.
Please use [OneHotEncoderEstimator](ml-features.html#onehotencoderestimator)
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.
+[One-hot encoding](http://en.wikipedia.org/wiki/One-hot) maps a column of
label indices to a column of binary vectors, and each output binary vector
includes 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` can transform multiple columns, returning a
one-hot-encoded output vector column for each input column.
--- End diff --
Perhaps we should add a note about vector assembling, something like "It is
common to merge these vectors into a single feature vector using
`VectorAssembler`"?
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