.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/preprocessing/plot_target_encoder.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_preprocessing_plot_target_encoder.py: ============================================ Comparing Target Encoder with Other Encoders ============================================ .. currentmodule:: sklearn.preprocessing The :class:`TargetEncoder` uses the value of the target to encode each categorical feature. In this example, we will compare three different approaches for handling categorical features: :class:`TargetEncoder`, :class:`OrdinalEncoder`, :class:`OneHotEncoder` and dropping the category. .. note:: `fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a cross fitting scheme is used in `fit_transform` for encoding. See the :ref:`User Guide `. for details. .. GENERATED FROM PYTHON SOURCE LINES 18-22 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 23-27 Loading Data from OpenML ======================== First, we load the wine reviews dataset, where the target is the points given be a reviewer: .. GENERATED FROM PYTHON SOURCE LINES 27-34 .. code-block:: Python from sklearn.datasets import fetch_openml wine_reviews = fetch_openml(data_id=42074, as_frame=True) df = wine_reviews.frame df.head() .. raw:: html
country description designation points price province region_1 region_2 variety winery
0 US This tremendous 100% varietal wine hails from ... Martha's Vineyard 96 235.0 California Napa Valley Napa Cabernet Sauvignon Heitz
1 Spain Ripe aromas of fig, blackberry and cassis are ... Carodorum Selección Especial Reserva 96 110.0 Northern Spain Toro NaN Tinta de Toro Bodega Carmen Rodríguez
2 US Mac Watson honors the memory of a wine once ma... Special Selected Late Harvest 96 90.0 California Knights Valley Sonoma Sauvignon Blanc Macauley
3 US This spent 20 months in 30% new French oak, an... Reserve 96 65.0 Oregon Willamette Valley Willamette Valley Pinot Noir Ponzi
4 France This is the top wine from La Bégude, named aft... La Brûlade 95 66.0 Provence Bandol NaN Provence red blend Domaine de la Bégude


.. GENERATED FROM PYTHON SOURCE LINES 35-37 For this example, we use the following subset of numerical and categorical features in the data. The target are continuous values from 80 to 100: .. GENERATED FROM PYTHON SOURCE LINES 37-53 .. code-block:: Python numerical_features = ["price"] categorical_features = [ "country", "province", "region_1", "region_2", "variety", "winery", ] target_name = "points" X = df[numerical_features + categorical_features] y = df[target_name] _ = y.hist() .. image-sg:: /auto_examples/preprocessing/images/sphx_glr_plot_target_encoder_001.png :alt: plot target encoder :srcset: /auto_examples/preprocessing/images/sphx_glr_plot_target_encoder_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 54-60 Training and Evaluating Pipelines with Different Encoders ========================================================= In this section, we will evaluate pipelines with :class:`~sklearn.ensemble.HistGradientBoostingRegressor` with different encoding strategies. First, we list out the encoders we will be using to preprocess the categorical features: .. GENERATED FROM PYTHON SOURCE LINES 60-73 .. code-block:: Python from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, TargetEncoder categorical_preprocessors = [ ("drop", "drop"), ("ordinal", OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1)), ( "one_hot", OneHotEncoder(handle_unknown="ignore", max_categories=20, sparse_output=False), ), ("target", TargetEncoder(target_type="continuous")), ] .. GENERATED FROM PYTHON SOURCE LINES 74-75 Next, we evaluate the models using cross validation and record the results: .. GENERATED FROM PYTHON SOURCE LINES 75-119 .. code-block:: Python from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.model_selection import cross_validate from sklearn.pipeline import make_pipeline n_cv_folds = 3 max_iter = 20 results = [] def evaluate_model_and_store(name, pipe): result = cross_validate( pipe, X, y, scoring="neg_root_mean_squared_error", cv=n_cv_folds, return_train_score=True, ) rmse_test_score = -result["test_score"] rmse_train_score = -result["train_score"] results.append( { "preprocessor": name, "rmse_test_mean": rmse_test_score.mean(), "rmse_test_std": rmse_train_score.std(), "rmse_train_mean": rmse_train_score.mean(), "rmse_train_std": rmse_train_score.std(), } ) for name, categorical_preprocessor in categorical_preprocessors: preprocessor = ColumnTransformer( [ ("numerical", "passthrough", numerical_features), ("categorical", categorical_preprocessor, categorical_features), ] ) pipe = make_pipeline( preprocessor, HistGradientBoostingRegressor(random_state=0, max_iter=max_iter) ) evaluate_model_and_store(name, pipe) .. GENERATED FROM PYTHON SOURCE LINES 120-126 Native Categorical Feature Support ================================== In this section, we build and evaluate a pipeline that uses native categorical feature support in :class:`~sklearn.ensemble.HistGradientBoostingRegressor`, which only supports up to 255 unique categories. In our dataset, the most of the categorical features have more than 255 unique categories: .. GENERATED FROM PYTHON SOURCE LINES 126-129 .. code-block:: Python n_unique_categories = df[categorical_features].nunique().sort_values(ascending=False) n_unique_categories .. rst-class:: sphx-glr-script-out .. code-block:: none winery 14810 region_1 1236 variety 632 province 455 country 48 region_2 18 dtype: int64 .. GENERATED FROM PYTHON SOURCE LINES 130-134 To workaround the limitation above, we group the categorical features into low cardinality and high cardinality features. The high cardinality features will be target encoded and the low cardinality features will use the native categorical feature in gradient boosting. .. GENERATED FROM PYTHON SOURCE LINES 134-164 .. code-block:: Python high_cardinality_features = n_unique_categories[n_unique_categories > 255].index low_cardinality_features = n_unique_categories[n_unique_categories <= 255].index mixed_encoded_preprocessor = ColumnTransformer( [ ("numerical", "passthrough", numerical_features), ( "high_cardinality", TargetEncoder(target_type="continuous"), high_cardinality_features, ), ( "low_cardinality", OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1), low_cardinality_features, ), ], verbose_feature_names_out=False, ) # The output of the of the preprocessor must be set to pandas so the # gradient boosting model can detect the low cardinality features. mixed_encoded_preprocessor.set_output(transform="pandas") mixed_pipe = make_pipeline( mixed_encoded_preprocessor, HistGradientBoostingRegressor( random_state=0, max_iter=max_iter, categorical_features=low_cardinality_features ), ) mixed_pipe .. raw:: html 
Pipeline(steps=[('columntransformer',
                     ColumnTransformer(transformers=[('numerical', 'passthrough',
                                                      ['price']),
                                                     ('high_cardinality',
                                                      TargetEncoder(target_type='continuous'),
                                                      Index(['winery', 'region_1', 'variety', 'province'], dtype='object')),
                                                     ('low_cardinality',
                                                      OrdinalEncoder(handle_unknown='use_encoded_value',
                                                                     unknown_value=-1),
                                                      Index(['country', 'region_2'], dtype='object'))],
                                       verbose_feature_names_out=False)),
                    ('histgradientboostingregressor',
                     HistGradientBoostingRegressor(categorical_features=Index(['country', 'region_2'], dtype='object'),
                                                   max_iter=20, random_state=0))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 165-166 Finally, we evaluate the pipeline using cross validation and record the results: .. GENERATED FROM PYTHON SOURCE LINES 166-168 .. code-block:: Python evaluate_model_and_store("mixed_target", mixed_pipe) .. GENERATED FROM PYTHON SOURCE LINES 169-172 Plotting the Results ==================== In this section, we display the results by plotting the test and train scores: .. GENERATED FROM PYTHON SOURCE LINES 172-204 .. code-block:: Python import matplotlib.pyplot as plt import pandas as pd results_df = ( pd.DataFrame(results).set_index("preprocessor").sort_values("rmse_test_mean") ) fig, (ax1, ax2) = plt.subplots( 1, 2, figsize=(12, 8), sharey=True, constrained_layout=True ) xticks = range(len(results_df)) name_to_color = dict( zip((r["preprocessor"] for r in results), ["C0", "C1", "C2", "C3", "C4"]) ) for subset, ax in zip(["test", "train"], [ax1, ax2]): mean, std = f"rmse_{subset}_mean", f"rmse_{subset}_std" data = results_df[[mean, std]].sort_values(mean) ax.bar( x=xticks, height=data[mean], yerr=data[std], width=0.9, color=[name_to_color[name] for name in data.index], ) ax.set( title=f"RMSE ({subset.title()})", xlabel="Encoding Scheme", xticks=xticks, xticklabels=data.index, ) .. image-sg:: /auto_examples/preprocessing/images/sphx_glr_plot_target_encoder_002.png :alt: RMSE (Test), RMSE (Train) :srcset: /auto_examples/preprocessing/images/sphx_glr_plot_target_encoder_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 205-229 When evaluating the predictive performance on the test set, dropping the categories perform the worst and the target encoders performs the best. This can be explained as follows: - Dropping the categorical features makes the pipeline less expressive and underfitting as a result; - Due to the high cardinality and to reduce the training time, the one-hot encoding scheme uses `max_categories=20` which prevents the features from expanding too much, which can result in underfitting. - If we had not set `max_categories=20`, the one-hot encoding scheme would have likely made the pipeline overfitting as the number of features explodes with rare category occurrences that are correlated with the target by chance (on the training set only); - The ordinal encoding imposes an arbitrary order to the features which are then treated as numerical values by the :class:`~sklearn.ensemble.HistGradientBoostingRegressor`. Since this model groups numerical features in 256 bins per feature, many unrelated categories can be grouped together and as a result overall pipeline can underfit; - When using the target encoder, the same binning happens, but since the encoded values are statistically ordered by marginal association with the target variable, the binning use by the :class:`~sklearn.ensemble.HistGradientBoostingRegressor` makes sense and leads to good results: the combination of smoothed target encoding and binning works as a good regularizing strategy against overfitting while not limiting the expressiveness of the pipeline too much. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 22.986 seconds) .. _sphx_glr_download_auto_examples_preprocessing_plot_target_encoder.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/preprocessing/plot_target_encoder.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/preprocessing/plot_target_encoder.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_target_encoder.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_target_encoder.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_target_encoder.zip ` .. include:: plot_target_encoder.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_