The boosting algorithm Adaboost (when using a tree) has three core parameters:
- number of weak learners to train
- learning rate
- max nb of splits (depth of tree)
What are good practices, perhaps proven empirically, for finding the appropriate value for these parameters?
One option I can think of is to do a grid search via cross validation and get out the validation accuracy (1-generalization error).