Dodging Performance Anomaly?

Running 50+ random forests with 50 trees each would be a lot of computation. I am first going to try seeing how well one RF predicts a GF or bearing offset.

Executing one tree classification is pretty fast though, isn't it? I don't think 2500 of them per tick seems unreasonable. Or is it many times more than that? My main 1v1 gun does up to 13k loop iterations for its kernel density calculation, and that's on top of the kd-tree search for 225 nearest neighbors among up to ~25k data points. And the gun is the fast part of the bot. :-)

The problem would be training the forests, which is much slower. Training one forest would be much easier, allowing you to do more training and perhaps have a higher tree count. I plan to eventually try both anyway, but I am doing the easier approach first.