Regression trees or Random Forest regressor with categorical inputs

Question

I have been trying to use a categorical inpust in a regression tree (or Random Forest Regressor) but sklearn keeps returning errors and asking for numerical inputs.

import sklearn as sk
MODEL = sk.ensemble.RandomForestRegressor(n_estimators=100)
MODEL.fit([('a',1,2),('b',2,3),('a',3,2),('b',1,3)], [1,2.5,3,4]) # does not work
MODEL.fit([(1,1,2),(2,2,3),(1,3,2),(2,1,3)], [1,2.5,3,4]) #works

MODEL = sk.tree.DecisionTreeRegressor()
MODEL.fit([('a',1,2),('b',2,3),('a',3,2),('b',1,3)], [1,2.5,3,4]) # does not work
MODEL.fit([(1,1,2),(2,2,3),(1,3,2),(2,1,3)], [1,2.5,3,4]) #works

To my understanding, categorical inputs should be possible in these methods without any conversion (e.g. WOE substitution).

Has anyone else had this difficulty?

thanks!

Answer 1

scikit-learn has no dedicated representation for categorical variables (a.k.a factors in R), one possible solution is to encode the strings as int using LabelEncoder:

import numpy as np
from sklearn.preprocessing import LabelEncoder  
from sklearn.ensemble import RandomForestRegressor

X = np.asarray([('a',1,2),('b',2,3),('a',3,2),('c',1,3)]) 
y = np.asarray([1,2.5,3,4])

# transform 1st column to numbers
X[:, 0] = LabelEncoder().fit_transform(X[:,0]) 

regressor = RandomForestRegressor(n_estimators=150, min_samples_split=2)
regressor.fit(X, y)
print(X)
print(regressor.predict(X))

Output:

[[ 0.  1.  2.]
 [ 1.  2.  3.]
 [ 0.  3.  2.]
 [ 2.  1.  3.]]
[ 1.61333333  2.13666667  2.53333333  2.95333333]

But remember that this is a slight hack if a and b are independent categories and it only works with tree-based estimators. Why? Because b is not really bigger than a. The correct way would be to use the OneHotEncoder after the LabelEncoder or pd.get_dummies yielding two separate, one-hot encoded columns for X[:, 0].

import numpy as np
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.ensemble import RandomForestRegressor

X = np.asarray([('a',1,2),('b',2,3),('a',3,2),('c',1,3)]) 
y = np.asarray([1,2.5,3,4])

# transform 1st column to numbers
import pandas as pd
X_0 = pd.get_dummies(X[:, 0]).values
X = np.column_stack([X_0, X[:, 1:]])

regressor = RandomForestRegressor(n_estimators=150, min_samples_split=2)
regressor.fit(X, y)
print(X)
print(regressor.predict(X))

Answer 2

You must dummy code by hand in python. I would suggest using pandas.get_dummies() for one hot encoding. For Boosted trees I have had success using factorize() to achieve Ordinal Encoding.

There is also a whole package for this sort of thing here.

For a more detailed explanation look in this Data Science Stack Exchange post.