gbm

How to find the optimum number of estimators using "OOB" method in sklearn boosting? Question: The gbm package in R has a function gbm.perf to find the optimum number of trees for the model using different methods like "Out-of-Bag" or "Cross-Validation" error, which helps to avoid over-fitting. Does Gradientboosting inScikit learn library in python also …