[joeyrichar]

As textminer says, RFs are really nice in that there are few parameters to tune, and the results typically are not that sensitive to the choice of those parameters (contrasted to, say, SVMs, where you can get killed in performance with a poor choice of tuning parameters).

With the MLaaS platform, all of the model optimization is taken care of under the hood (we also allow users to do their own parameter selection / tuning if desired). Our super fast implementation, WiseRF (10-100x faster than RF in sklearn or R) enables us to efficiently explore the hyperparameter space.

Thanks again for your questions and comments.

Joey Richards, Chief Scientist, wise.io

[mbq]

Two more questions then; is this speed-up achieved on a single core or also due to parallelism? And have you compared WiseRF with Random Jungle (http://bioinformatics.oxfordjournals.org/content/26/14/1752....)?

[joeyrichar]

(1) The speed-up is achieved on a single core (multithreaded). The tiny memory footprint enables us to do embedded learning (e.g., on an ARM chip). We also have a distributed version of WiseRF in development (stay tuned!).

(2) Soon, we'll be publishing a series of blog posts to benchmark WiseRF against competing implementations. Look for that next week.

Thanks for your interest!