Passing categorical data to Sklearn Decision Tree

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Able to handle both numerical and categorical data.

This only means that you can use

• the DecisionTreeClassifier class for classification problems
• the DecisionTreeRegressor class for regression.

In any case you need to one-hot encode categorical variables before you fit a tree with sklearn, like so:

import pandas as pd
from sklearn.tree import DecisionTreeClassifier

data = pd.DataFrame()
data['A'] = ['a','a','b','a']
data['B'] = ['b','b','a','b']
data['C'] = [0, 0, 1, 0]
data['Class'] = ['n','n','y','n']

tree = DecisionTreeClassifier()

one_hot_data = pd.get_dummies(data[['A','B','C']],drop_first=True)
tree.fit(one_hot_data, data['Class'])

For nominal categorical variables, I would not use LabelEncoderbut sklearn.preprocessing.OneHotEncoder or pandas.get_dummies instead because there is usually no order in these type of variables.

(This is just a reformat of my comment above from 2016...it still holds true.)

The accepted answer for this question is misleading.

As it stands, sklearn decision trees do not handle categorical data - see issue #5442.

The recommended approach of using Label Encoding converts to integers which the DecisionTreeClassifier() will treat as numeric. If your categorical data is not ordinal, this is not good - you'll end up with splits that do not make sense.

Using a OneHotEncoder is the only current valid way, allowing arbitrary splits not dependent on the label ordering, but is computationally expensive.