The documentation for fitting classification trees states that X needs to be a floating point array, but also indicates that X can represent categorical variables (using the 'CategoricalPredictors' Name-Value argument).
Is the proper way to handle this to
(1) take the categorical variable, e.g.
category1 = {'duck','duck','goose','squash','quartz'}';category2 = {'animal','animal','animal','vegetable','mineral'}';
(2) run those through grp2idx()
numcat1 = grp2idx(category1);numcat2 = grp2idx(category2);
(3) Embed those in my X:
X = [numcat1 numcat2 otherTrulyNumericalVariables]
(4) Identify those as categorical
tree = ClassificationTree.fit(X,Y,'CategoricalPredictors',[1 2])
Seems like that's probably right, but I'd love an expert to vet that idea. The documentation doesn't have a categorical example.
Best Answer