  The MUST (Mass Unspecific Supervised Tagging) method has proven to be
successful in implementing generic jet taggers capable of discriminating
various signals over a wide range of jet masses. We implement the MUST concept
by using eXtreme Gradient Boosting ({\tt XGBoost}) classifiers instead of
neural networks (NNs) as previously done. We build both fully-generic and
specific multi-pronged taggers, to identify 2, 3, and/or 4-pronged signals from
SM QCD background. We show that {\tt XGBoost}-based taggers are not only easier
to optimize and much faster than those based in NNs, but also show quite
similar performance, even when testing with signals not used in training,
providing an alternative machine-learning implementation that can potentially
be used to test the robustness of the results.
