Hi Everyone,
I am creating a decision tree and I want it to be only 3 nodes deep. I am controlling the splitting by using the leaf size under the Node area for Decision tree models, I am setting it to 200 (that is a minimum of 200 obs per leaf). The issue is the target I am splitting up which is binary (1 and 2) has a low volume. The target 2 only account for 0.44% of the observations. This is creating difficulty in the tree splitting. When I set the leaf size to 200 it goes to deep but when I increase it the tree does not split at all. I have tried changing the Significance level but it has no affect.
Nominal target Criterion is ProbChisq and significance level is 0.2.
Any help on how to tune the Hyper-Parameters to prune the model would be great.
Thanks
Scott
