Can sklearn random forest directly handle categorical features?
You have to make the categorical variable into a series of dummy variables. Yes I know its annoying and seems unnecessary but that is how sklearn works. 
if you are using pandas. use pd.get_dummies, it works really well.
Say I have a categorical feature, color, which takes the values
['red', 'blue', 'green', 'orange'],
and I want to use it to predict something in a random forest.  If I one-hot encode it (i.e. I change it to four dummy variables), how do I tell sklearn that the four dummy variables are really one variable?  Specifically, when sklearn is randomly selecting features to use at different nodes, it should either include the red, blue, green and orange dummies together, or it shouldn't include any of them.
I've heard that there's no way to do this, but I'd imagine there must be a way to deal with categorical variables without arbitrarily coding them as numbers or something like that.