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Plot Target Encoder

============================================ 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 <target_encoder> for details.

Imports for Comparing Categorical Encoding Strategies

Target encoding replaces categories with smoothed target statistics: TargetEncoder encodes each categorical level as the mean of the target variable for that category, smoothed toward the global mean using an additive smoothing formula that prevents overfitting on rare categories. Unlike OrdinalEncoder (which assigns arbitrary integers, imposing a false ordering) and OneHotEncoder (which creates a binary column per category, exploding dimensionality for high-cardinality features), target encoding produces a single numeric feature per categorical column that directly captures the category’s relationship with the target. The critical subtlety is that fit_transform uses internal cross-fitting (encoding each fold with statistics from other folds) to prevent target leakage, while fit followed by transform does not – this distinction is essential for avoiding overfitting in training pipelines.

Cardinality determines the optimal encoding strategy: The Wine Reviews dataset contains categorical features with cardinalities ranging from a few dozen (country, variety) to tens of thousands (winery, region). OneHotEncoder with max_categories=20 caps dimensionality expansion but loses information from rare categories. OrdinalEncoder preserves all categories compactly but the arbitrary integer ordering confuses models that assume numeric features are continuous. TargetEncoder handles high cardinality naturally by collapsing categories to a meaningful numeric scale, and when combined with HistGradientBoostingRegressor’s 256-bin histogram discretization, the smoothed target-encoded values create bins that group statistically similar categories together. The ColumnTransformer orchestrates different encoding strategies for numerical versus categorical features, enabling the mixed encoding approach where low-cardinality features use native categorical support while high-cardinality features are target encoded.

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

# %%
# Loading Data from OpenML
# ========================
# First, we load the wine reviews dataset, where the target is the points given
# be a reviewer:
from sklearn.datasets import fetch_openml

wine_reviews = fetch_openml(data_id=42074, as_frame=True)

df = wine_reviews.frame
df.head()

# %%
# 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:
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()

# %%
# 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:
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")),
]

# %%
# Next, we evaluate the models using cross validation and record the results:
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)


# %%
# 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:
n_unique_categories = df[categorical_features].nunique().sort_values(ascending=False)
n_unique_categories

# %%
# 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.
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

# %%
# Finally, we evaluate the pipeline using cross validation and record the results:
evaluate_model_and_store("mixed_target", mixed_pipe)

# %%
# Plotting the Results
# ====================
# In this section, we display the results by plotting the test and train scores:
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,
    )

# %%
# 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.