How can I fit categorical data types for random forest classification? I need to find the accuracy of a training dataset by applying Random Forest Algorithm. But my the type of my data set are both categorical and numeric. When I tried to fit those data, I get an error.


  'Input contains NaN, infinity or a value too large for dtype('float32')'.


May be the problem is for object data types. How can I fit categorical data without transforming for applying RF?

Here's my code.
You need to convert the categorical features into numeric attributes. A common approach is to use one-hot encoding, but that's definitely not the only option. If you have a variable with a high number of categorical levels, you should consider combining levels or using the hashing trick. Sklearn comes equipped with several approaches (check the "see also" section): One Hot Encoder and Hashing Trick

If you're not committed to sklearn, the h2o random forest implementation handles categorical features directly.