Pushing the docs to dev/ for branch: main, commit 52236699f6ecafa8b17415f6a7d14c1bdc2b68ef

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Column Transformer with Mixed Types\n\n.. currentmodule:: sklearn\n\nThis example illustrates how to apply different preprocessing and feature\nextraction pipelines to different subsets of features, using\n:class:`~compose.ColumnTransformer`. This is particularly handy for the\ncase of datasets that contain heterogeneous data types, since we may want to\nscale the numeric features and one-hot encode the categorical ones.\n\nIn this example, the numeric data is standard-scaled after mean-imputation,\nwhile the categorical data is one-hot encoded after imputing missing values\nwith a new category (``'missing'``).\n\nIn addition, we show two different ways to dispatch the columns to the\nparticular pre-processor: by column names and by column data types.\n\nFinally, the preprocessing pipeline is integrated in a full prediction pipeline\nusing :class:`~pipeline.Pipeline`, together with a simple classification\nmodel.\n"
+        "\n# Column Transformer with Mixed Types\n\n.. currentmodule:: sklearn\n\nThis example illustrates how to apply different preprocessing and feature\nextraction pipelines to different subsets of features, using\n:class:`~compose.ColumnTransformer`. This is particularly handy for the\ncase of datasets that contain heterogeneous data types, since we may want to\nscale the numeric features and one-hot encode the categorical ones.\n\nIn this example, the numeric data is standard-scaled after mean-imputation. The\ncategorical data is one-hot encoded via ``OneHotEncoder``, which\ncreates a new category for missing values.\n\nIn addition, we show two different ways to dispatch the columns to the\nparticular pre-processor: by column names and by column data types.\n\nFinally, the preprocessing pipeline is integrated in a full prediction pipeline\nusing :class:`~pipeline.Pipeline`, together with a simple classification\nmodel.\n"
       ]
     },
     {

dev/_downloads/26f110ad6cff1a8a7c58b1a00d8b8b5a/plot_column_transformer_mixed_types.ipynb

@@ -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.