.. 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-validation scheme is used in `fit_transform` for encoding. See the :ref:`User Guide `. for details. .. GENERATED FROM PYTHON SOURCE LINES 20-24 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 24-31 .. code-block:: default from sklearn.datasets import fetch_openml wine_reviews = fetch_openml(data_id=42074, as_frame=True, parser="pandas") df = wine_reviews.frame df.head() .. GENERATED FROM PYTHON SOURCE LINES 32-34 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 34-50 .. code-block:: default 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() .. GENERATED FROM PYTHON SOURCE LINES 51-57 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 57-72 .. code-block:: default from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OrdinalEncoder from sklearn.preprocessing import OneHotEncoder from sklearn.preprocessing import 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 73-74 Next, we evaluate the models using cross validation and record the results: .. GENERATED FROM PYTHON SOURCE LINES 74-118 .. code-block:: default from sklearn.pipeline import make_pipeline from sklearn.model_selection import cross_validate from sklearn.ensemble import HistGradientBoostingRegressor 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 119-125 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 125-128 .. code-block:: default n_unique_categories = df[categorical_features].nunique().sort_values(ascending=False) n_unique_categories .. GENERATED FROM PYTHON SOURCE LINES 129-133 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 133-163 .. code-block:: default 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 .. GENERATED FROM PYTHON SOURCE LINES 164-165 Finally, we evaluate the pipeline using cross validation and record the results: .. GENERATED FROM PYTHON SOURCE LINES 165-167 .. code-block:: default evaluate_model_and_store("mixed_target", mixed_pipe) .. GENERATED FROM PYTHON SOURCE LINES 168-171 Plotting the Results ==================== In this section, we display the results by plotting the test and train scores: .. GENERATED FROM PYTHON SOURCE LINES 171-203 .. code-block:: default 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, ) .. GENERATED FROM PYTHON SOURCE LINES 204-228 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 0.000 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/main?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/?path=auto_examples/preprocessing/plot_target_encoder.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_target_encoder.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_target_encoder.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_