Boosting usa bootstraping? I had a question about boosting. When in the first iteration of the algorithm we pass our data to the first decision tree, this data we pass is a sample generated by bootstraping or is it the original data?
Another question I have is: After passing the initial dataset to the first decision tree, the errors in the original dataset are weighted giving more weight to them. In the second iteration (already with the modified weights in the observations with error) this whole set is passed? or a subsample generated with bootstrap is passed in which the probability that the errors appear, having more weight, is higher?

We always subsample our original training set in each booster iteration. Usually this is controlled by a parameter like: bagging, bagging_fraction, or subsample; of course we might set that parameter to 1.0 so we do no subsampling but thst is a special case. This subsampling is irrespective of the errors. What happens is that after the subsampling, that new subsample is weighted based on the errors/gradient computed up to that point.
