Github user MLnick commented on a diff in the pull request: https://github.com/apache/spark/pull/20257#discussion_r161989107 --- 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. --- End diff -- I don't really like this description as I think it conflates the core of what one-hot-encoding does with the implementation detail of dataframe columns (which we refer to in the next paragraph anyway). How about "`[OHE](...)` maps a categorical feature, represented as a label index, to a binary vector with at most a single one-value indicating the presence of the feature."
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