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MeanEncoder#

The MeanEncoder() replaces categories with the mean of the target per category. For example, if we are trying to predict default rate, and our data has the variable city, with categories, London, Manchester and Bristol, and the default rate per city is 0.1, 0.5, and 0.3, respectively, the encoder will replace London by 0.1, Manchester by 0.5 and Bristol by 0.3.

The motivation is to try and create a monotonic relationship between the target and the encoded categories. This tends to help improve performance of linear models.

Let’s look at an example using the Titanic Dataset.

First, let’s load the data and separate it into train and test:

from sklearn.model_selection import train_test_split
from feature_engine.datasets import load_titanic
from feature_engine.encoding import MeanEncoder

X, y = load_titanic(
    return_X_y_frame=True,
    handle_missing=True,
    predictors_only=True,
    cabin="letter_only",
)

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=0,
)

print(X_train.head())

We see the resulting data below:

      pclass     sex        age  sibsp  parch     fare cabin embarked
501        2  female  13.000000      0      1  19.5000     M        S
588        2  female   4.000000      1      1  23.0000     M        S
402        2  female  30.000000      1      0  13.8583     M        C
1193       3    male  29.881135      0      0   7.7250     M        Q
686        3  female  22.000000      0      0   7.7250     M        Q

Now, we set up the MeanEncoder() to replace the categories only in the 3 indicated variables:

encoder = MeanEncoder(
    variables=['cabin', 'pclass', 'embarked'],
    ignore_format=True,
)

encoder.fit(X_train, y_train)

With fit() the encoder learns the target mean value for each category, which are stored in its encoder_dict_ parameter:

encoder.encoder_dict_

The encoder_dict_ contains the mean value of the target per category, per variable. So we can easily use this dictionary to map the numbers to the original labels.

{'cabin': {'A': 0.5294117647058824,
        'B': 0.7619047619047619,
        'C': 0.5633802816901409,
        'D': 0.71875,
        'E': 0.71875,
        'F': 0.6666666666666666,
        'G': 0.5,
        'M': 0.30484330484330485,
        'T': 0.0},
'pclass': {1: 0.6173913043478261,
        2: 0.43617021276595747,
        3: 0.25903614457831325},
'embarked': {'C': 0.553072625698324,
        'Missing': 1.0,
        'Q': 0.37349397590361444,
        'S': 0.3389570552147239}}

We can now go ahead and replace the original strings with the numbers:

train_t = encoder.transform(X_train)
test_t = encoder.transform(X_test)

print(train_t.head())

Below we see the resulting dataframe, where the original variable values are now replaced with the target mean:

        pclass     sex        age  sibsp  parch     fare     cabin  embarked
501   0.436170  female  13.000000      0      1  19.5000  0.304843  0.338957
588   0.436170  female   4.000000      1      1  23.0000  0.304843  0.338957
402   0.436170  female  30.000000      1      0  13.8583  0.304843  0.553073
1193  0.259036    male  29.881135      0      0   7.7250  0.304843  0.373494
686   0.259036  female  22.000000      0      0   7.7250  0.304843  0.373494

Handling Cardinality#

The MeanEncoder() replaces categories with the mean of the target per category. If the variable has low cardinality, then there is a fair representation of each label in the dataset, and the mean target value per category can be determined with some certainty. However, if variables are highly cardinal, with only very few observations for some labels, then the mean target value for those categories will be unreliable.

To encode highly cardinal variables using target mean encoding, we could group infrequent categories first using the RareLabelEncoder().

Alternatively, the MeanEncoder() provides an option to “smooth” the mean target value estimated for rare categories. In these cases, the target estimates can be determined as a mixture of two values: the mean target value per category (the posterior) and the mean target value in the entire dataset (the prior).

\[mapping = (w_i) posterior + (1-w_i) prior\]

The two values are “blended” using a weighting factor (wi) which is a function of the category group size and the variance of the target in the data (t) and within the category (s):

\[w_i = n_i t / (s + n_i t)\]

When the category group is large, the weighing factor aroximates 1 and therefore more weight is given to the posterior. When the category group size is small, then the weight becomes relevant and more weight is given to the prior.

Finally, you may want to check different implementations of encoding methods that use blends of probabilities, which are available in the open-source package Category encoders through the transformers M-estimate and Target Encoder.

More details#

In the following notebook, you can find more details into the MeanEncoder() functionality and example plots with the encoded variables:

For more details about this and other feature engineering methods check out these resources:

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