Hi,
I believe that Caret uses a grid-serach approach. I was wondering if:
1 There are more efficient implementations for HP tuning for classification
algos (eg XGboost, CatBoost, SVM, RF etc), using say GM/SWARM approaches, akin
to Google's approach AutoML for Image related Net problems?
2 This one is most probably wishful thinking, but is anyone looking at GM/SWARM
at HP tuning across models (ensemble models). eg the best set of HP for
combined XGBoost + SVM, which accounts for the correlation/interaction of the
prediction assumptions.
BestAlex
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