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# Comparing Target Encoder with Other Encoders

The `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: `TargetEncoder`, `OrdinalEncoder`, `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 User Guide. for details.

## 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, parser="pandas")

    df = wine_reviews.frame
    df.head()

| 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

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 `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 `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

    winery      14810
    region_1     1236
    variety       632
    province      455
    country        48
    region_2       18
    dtype: int64

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

    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))])

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Pipeline

    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))])

columntransformer: 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)

numerical

    ['price']

passthrough

    passthrough

high_cardinality

    Index(['winery', 'region_1', 'variety', 'province'], dtype='object')

TargetEncoder

    TargetEncoder(target_type='continuous')

low_cardinality

    Index(['country', 'region_2'], dtype='object')

OrdinalEncoder

    OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)

HistGradientBoostingRegressor

    HistGradientBoostingRegressor(categorical_features=Index(['country', 'region_2'], dtype='object'),
                                  max_iter=20, random_state=0)

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 `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 `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.

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