The DecisionTreeEncoder() replaces categories in the variable with the predictions of a decision tree.

The transformer first encodes categorical variables into numerical variables using OrdinalEncoder(). You have the option to have the integers assigned to the categories as they appear in the variable, or ordered by the mean value of the target per category. You can regulate this behaviour with the parameter encoding_method. As decision trees are able to pick non-linear relationships, replacing categories by arbitrary numbers should be enough in practice.

After this, the transformer fits with this numerical variable a decision tree to predict the target variable. Finally, the original categorical variable is replaced by the predictions of the decision tree.

The motivation of the DecisionTreeEncoder() is to try and create monotonic relationships between the categorical variables and the target.

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

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

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

from feature_engine.encoding import DecisionTreeEncoder

# Load dataset
def load_titanic():
        data = pd.read_csv('')
        data = data.replace('?', np.nan)
        data['cabin'] = data['cabin'].astype(str).str[0]
        data['pclass'] = data['pclass'].astype('O')
        data['embarked'].fillna('C', inplace=True)
        return data

data = load_titanic()

# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
                data.drop(['survived', 'name', 'ticket'], axis=1),
                data['survived'], test_size=0.3, random_state=0)

X_train[['cabin', 'pclass', 'embarked']].head(10)

We will encode the following categorical variables:

      cabin pclass embarked
501      n      2        S
588      n      2        S
402      n      2        C
1193     n      3        Q
686      n      3        Q
971      n      3        Q
117      E      1        C
540      n      2        S
294      C      1        C
261      E      1        S

We set up the encoder to encode the variables above with 3 fold cross-validation, using a grid search to find the optimal depth of the decision tree (this is the default behaviour of the DecisionTreeEncoder()). In this example, we optimize the tree using the roc-auc metric.

# set up the encoder
encoder = DecisionTreeEncoder(
     variables=['cabin', 'pclass', 'embarked'],

# fit the encoder, y_train)

With fit() the DecisionTreeEncoder() fits 1 decision tree per variable. Now we can go ahead and transform the categorical variables into numbers, using the predictions of these trees:

# transform the data
train_t = encoder.transform(X_train)
test_t = encoder.transform(X_test)

train_t[['cabin', 'pclass', 'embarked']].head(10)

We can see the encoded variables below:

         cabin    pclass  embarked
501   0.304843  0.436170  0.338957
588   0.304843  0.436170  0.338957
402   0.304843  0.436170  0.558011
1193  0.304843  0.259036  0.373494
686   0.304843  0.259036  0.373494
971   0.304843  0.259036  0.373494
117   0.611650  0.617391  0.558011
540   0.304843  0.436170  0.338957
294   0.611650  0.617391  0.558011
261   0.611650  0.617391  0.338957

More details#

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

All notebooks can be found in a dedicated repository.