Hi Cat. Grats on the tool and the approach. Love to see this kind of innovations in this space. But (you know there was a 'but' coming, didn't you), as I understand 'Best in Bags' relies on the computed 'Stat Weights'. Now, the problem I have with this is that 'stat weights', while being accurate in at a very small distance form the current gear point, in many cases do not predict very well the outcomes when extrapolated to distances typical of even replacing one item such as a ring or neck (I main Fire Mage, so this might be class specific). I guess the same reasoning behind AMR's ML approach applies here as well. The 'fitness landscape' is rugged, so extrapolating from a local gradient might with significant probability land you somewhere far from optimal, and even worse than where you started from. Could utulizing data from ML be a way to go to improve 'Best in Bags'?