Can you help me with Summary of Boosted Trees Graph? The manual says: "Summary button to display a graph of the average squared prediction error over the successive boosting steps" and in regression problem I've understood how to calculate this term for every point and I can calculate it, but in classification problem I can't understand how to calculate the point. Could you help me and give me the formula?
Thank you for your answer, but my problem is not solved:(
In the attachement there is an example with probability and deviance loss function based on results of first step of boosting tree. The result of the first step is wrong. Could you please help me and tell me what could be wrong in my calculations?
The algorithm of the boosted classification trees can be found in the text book: Hastie, Tibshirani, and Friedman. "Boosting and Additive Trees." The Elements of statistical learning. 2nd ed. New York: Springer, 2009. 348-349. Print. In particular, (10.20) ~ (10.22) covers the algorithm of conditional probability and deviance loss function.
is there any news on my question? I really need help on this. Thanks for any help, in advance!
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