Can I extract the underlying decision-rules (or 'decision paths') from a trained tree in a decision tree as a textual list?
Something like:
if A>0.4 then if B<0.2 then if... moreCan I extract the underlying decision-rules (or 'decision paths') from a trained tree in a decision tree as a textual list?
Something like:
if A>0.4 then if B<0.2 then if C>0.8 then class='X'
I am writing my own code for a decision tree. I need to decide on when to terminate the tree building process. I could think of limiting the height of the tree, but this seems... moreI am writing my own code for a decision tree. I need to decide on when to terminate the tree building process. I could think of limiting the height of the tree, but this seems trivial. Could anyone give me a better idea on how to implement my termination function.
Here in my tree building algorithm.