Github user MLnick commented on a diff in the pull request: https://github.com/apache/spark/pull/20257#discussion_r161741274 --- 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. --- End diff -- "can handle multi-column. By specifying ..." -> "can transform multiple columns, returning a one-hot-encoded output ..."
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