 model, sort
of the centered toolbox. Sometimes you need to tune a little
bit more, so you need to tune the depth of the trees or the
learning rate, and so on. But if you tune them, then they
will usually beat random forest on those data sets.

One case where you might not want to use trees is if you
have very high dimensional sparse data then linear models
might work better but also your mileage may vary. On the
contrary, if you have like a low dimensional space,
tree-based models are probably a good bet.
