# How to use AdaBoost weights in Decision Tree

I've implement c4.5 and CART, and i want compare them in my implementation of Boosting (AdaBoost). I don't understand how to "connect" weights, that im setting in AdaBoost, to decision tree algorithms. At the begining i have Data set that i split for trening and testing sets. I set weights for trening set and build trees, then update weights, but what next? Should i add some weight parameter to gain ratio and gini index of trees? Or i should create traning set by sampling with or without replacment and use only part of Training set to build next tree?

• Your tree algorithm needs to take into account the weights. – Matthew Drury May 13 '17 at 23:40

AdaBoost for classification is essentially to build multiple weak learners based on re-weighted samples and the final classifier is weighted combination of all the weak learners.