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Question says most of it. I created a matrix of descriptors, set the vectors of responses, and input a set number of iterations. Each time I run the function with the same exact inputs, I get the same confusion table, but I get a different training error graph and different variable importance plots.

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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.

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  • $\begingroup$ My samples are fixed. However, what you're saying still makes sense in a fixed data set. I think it has to do with the fact that the ada package in R uses stochastic boosting. $\endgroup$ – amustafa Oct 19 '15 at 4:16
  • $\begingroup$ Boosting samples bootstrap subsets of your data. So if it samples, if You don't fix set.seed(123) or any other number You will receive different results of sampling which leads to different model results $\endgroup$ – Marcin Kosiński Oct 28 '15 at 18:52

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