That's pretty interesting, why are boosted decision trees so effective? I've heard the same meme applied to kaggle competitions (everything is just a way to shove data into xgboost, etc)
The grandparent post contains a hint of something I've already heard in lectures and from ML practicioners: boosting and random forests are more resilient to improper tuning (to put it another way, they are more universal and work well "out of the box").
Which, BTW, makes them more appealing to me personally. In many real-life cases extra few percent of accuracy matters very little, but ability to just apply something to a problem without much fuss matters a lot.
Which, BTW, makes them more appealing to me personally. In many real-life cases extra few percent of accuracy matters very little, but ability to just apply something to a problem without much fuss matters a lot.