4 ms·

I'm not very experienced in Machine learning, just dabbled around a bit, so maybe someone could explain me this: Looking at the graph number of trees vs accura

by folli 12y ago

I'm not very experienced in Machine learning, just dabbled around a bit, so maybe someone could explain me this: Looking at the graph number of trees vs accuracy, I would have expected that the line would asymptotically reach a maximum accuracy given more and more trees; however for weka it looks quite wavy and for mahout it even looks as if there's an optimum and more trees are worse. Or is it just noise and I'm interpreting too much?

dthal 12y ago
Usually RF will improve up to some point, and then the test (or OOB) accuracy will rattle around a little bit, centered around some final value. RF should never overfit from having too many trees. Both plots start at 50 trees, so it looks like the 'improve' part of that is over by then, and what you are seeing here is variation due to different trees being slightly better/worse. Incidentally, that means that the variation in both plots is probably not meaningful and that 'best' score of 99.4% at 250 trees is probably basically an outlier.