Passing categorical data to Sklearn Decision Tree

Question

There are several posts about how to encode categorical data to Sklearn Decision trees, but from Sklearn documentation, we got these

Some advantages of decision trees are:

(...)

Able to handle both numerical and categorical data. Other techniques are usually specialized in analyzing datasets that have only one type of variable. See the algorithms for more information.

But running the following script

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()
tree.fit(data[['A','B','C']], data['Class'])

outputs the following error:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/site-packages/sklearn/tree/tree.py", line 154, in fit
    X = check_array(X, dtype=DTYPE, accept_sparse="csc")
  File "/usr/local/lib/python2.7/site-packages/sklearn/utils/validation.py", line 377, in check_array
    array = np.array(array, dtype=dtype, order=order, copy=copy)
ValueError: could not convert string to float: b

I know that in R it is possible to pass categorical data, with Sklearn, is it possible?

Answer 1

(This is just a reformat of my comment 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.

Answer 2

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'])

Answer 3

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.

Answer 4

As of v0.24.0, scikit supports the use of categorical features in HistGradientBoostingClassifier and HistGradientBoostingRegressor natively!

To enable categorical support, a boolean mask can be passed to the categorical_features parameter, indicating which feature is categorical. In the following, the first feature will be treated as categorical and the second feature as numerical:

>>> gbdt = HistGradientBoostingClassifier(categorical_features=[True, False])

Equivalently, one can pass a list of integers indicating the indices of the categorical features:

>>> gbdt = HistGradientBoostingClassifier(categorical_features=[0])

You still need to encode your strings, otherwise you will get "could not convert string to float" error. See here for an example on using OrdinalEncoder to convert strings to integers.

Answer 5

Yes decision tree is able to handle both numerical and categorical data. Which holds true for theoretical part, but during implementation, you should try either OrdinalEncoder or one-hot-encoding for the categorical features before training or testing the model. Always remember that ml models don't understand anything other than Numbers.

Answer 6

Sklearn Decision Trees do not handle conversion of categorical strings to numbers. I suggest you find a function in Sklearn (maybe this) that does so or manually write some code like:

def cat2int(column):
    vals = list(set(column))
    for i, string in enumerate(column):
        column[i] = vals.index(string)
    return column

Answer 7

With sklearn classifiers, you can model categorical variables both as an input and as an output.

Let's assume you have categorical predictors and categorical labels (i.e. multi-class classification task). Moreover, you want to handle missing or unknown labels for both predictors and labels.

First thing you need encoder like OrdinalEncoder.

Basic example:

# encoders
from sklearn.preprocessing import OrdinalEncoder

input_enc = OrdinalEncoder(unknown_value=-1, handle_unknown='use_encoded_value', encoded_missing_value=-1)
output_enc = OrdinalEncoder(unknown_value=-1, handle_unknown='use_encoded_value', encoded_missing_value=-1 )

input_enc.fit(df[['Attribute A','Attribute B']].values)
output_enc.fit(df[['Label']].values)


# build classifier
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(random_state=0)

X = input_enc.transform(df[['Attribute A','Attribute B']].values)
Y = output_enc.transform(df[['Label']].values)

clf.fit(X, Y)

# predict

predicted = clf.predict(input_enc.transform([('Value 1', 'Value 2')]))
predicted_label = output_enc.inverse_transform([predicted])

If you use df[...].values, your encoder will not store attribute names (column names). This does not matter, as long as same format is used for enc.transform() or enc.inverse_transofrm() (otherwise you will a warning).

OrdinalEncoder by default does not handle nan values and they are not handled by cls.fit(). This is solved by encoded_missing_value param.

In prediction phase, by default encoder will throw an error when ask to transform unknown labels. This is handled by handle_unknown param.

Answer 8

I would prefer to use tools provided by Scikit-Learn for this purpose. The main reason for doing so is that they can be easily integrated in a Pipeline.

Scikit-Learn itself provides very good classes to handle categorical data. Instead of writing your custom function, you should use LabelEncoder which is specially designed for this purpose.

Refer to the following code from the documentation:

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(["paris", "paris", "tokyo", "amsterdam"])
le.transform(["tokyo", "tokyo", "paris"]) 

This automatically encodes them into numbers for your machine learning algorithms. Now this also supports going back to strings from integers. You can do this by simply calling inverse_transform as follows:

list(le.inverse_transform([2, 2, 1]))

This would return ['tokyo', 'tokyo', 'paris'].

Also note that for many other classifiers, apart from decision trees, such as logistic regression or SVM, you would like to encode your categorical variables using One-Hot encoding. Scikit-learn supports this as well through the OneHotEncoder class.