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Hi! Thanks for the kind words!
I believe that the reason our model under-performed on Task 2 is that we ran out of time and couldn’t make the standard checks for over-fitting. By the time we had the final ensemble, the DEV set was already offline, so we couldn’t really see its performance on an out-of-sample data. This meant that we either had to go with the best single component (evaluated on DEV) or take a leap of faith and submit the ensemble on TEST.
We decided that whether we win or lose, we’ll do it as a team, so we went with the combined model. Sadly, the ship sank with everyone on board 🙂