DOC update example wording now that OneHotEncoder handles missing values (scikit-learn#23054)

deargle · web-flow · commit 52236699f6ec · 2022-04-20T14:51:19.000+02:00
diff --git a/examples/compose/plot_column_transformer_mixed_types.py b/examples/compose/plot_column_transformer_mixed_types.py
@@ -11,9 +11,9 @@
 case of datasets that contain heterogeneous data types, since we may want to
 scale the numeric features and one-hot encode the categorical ones.
 
-In this example, the numeric data is standard-scaled after mean-imputation,
-while the categorical data is one-hot encoded after imputing missing values
-with a new category (``'missing'``).
+In this example, the numeric data is standard-scaled after mean-imputation. The
+categorical data is one-hot encoded via ``OneHotEncoder``, which
+creates a new category for missing values.
 
 In addition, we show two different ways to dispatch the columns to the
 particular pre-processor: by column names and by column data types.
