this is the naive approach. deep learning tends to miss the random forest for the decision treeshttps://twitter.com/Magic_Flowers/status/1392171257970388993 …
I prefer to aggressively prune my decision trees to minimize risk of a random forest fire, you do need to prune at least once/year though because those things grow like hell
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wanna know how i know you’re not properly hydrating your models? pruning can be replaced by a simply map->deduce where the deduction step derives in a jacobian-gradient manner. this is basic stuff
Thanks. Twitter will use this to make your timeline better. UndoUndo
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