How to create one final decision tree
Hello,
I need to create on final decision tree based on 25 different decision trees but I am not sure how to do that. They need to be constructed in a sequential manner where we update the weights of the training examples based on the prediction and the error rate of the previous decision. Right now I have the random forest operator where I have 25 different decision trees but what is it that I used to create one final decision tree?
Thank you
I need to create on final decision tree based on 25 different decision trees but I am not sure how to do that. They need to be constructed in a sequential manner where we update the weights of the training examples based on the prediction and the error rate of the previous decision. Right now I have the random forest operator where I have 25 different decision trees but what is it that I used to create one final decision tree?
Thank you
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The process you're describing is implemented in ensemble models like Bagging and Boosting, and in Gradient Boosted Trees.
However, these create *separate* trees. I don't see how one decision tree would be able to express all the different trees adequately and I don't know an algorithm implementing this.
Regards,
Balázs