0xhfff 0xhfff - 3 months ago 44x
Python Question

Passing categorical data to Sklearn Decision Tree

There are several posts about how to encode categorical data to Sklearn Decission 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 specialised in analysing datasets that have only one type of variable. See algorithms for more information.

But running the fallowing 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'])

one get the fallowing 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?


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