Why does ada (adaboost) in R return different training error graphs and variable importance plots when running the same function multiple times?
Boosting, together with bagging, falls into the realm of so cold ensemble models: you randomly draw a sample from the data, fit a model, adjust your predictions, sample once again. Unless your samples are fixed, every time you run the algo you'll get slightly different results.