Benchmarking Random Forest Classification

If you look at the implementation for ski-learn, each tree emits a normalised probability vector for each prediction, those vectors are simply multiplied together to get the aggregate prediction, so its not very difficult to do yourself.

Although regardless, you are applying a batch learning technique anyway. You want an incremental learner for big data.

We have been working hard to reduce computing times and memory footprint (though, there is still a lot of improvement on that side).

(Unfortunately, I cannot run your benchmarks myself, because the compiled version of WiseRF requires a newer version of glibc than the one on my cluster, and crashes.)

For me, the promise of in-the-cloud machine learning is that I can call 'train' method, and specify one single hyperparameter: training budget (i.e. $). Perhaps also the max time before I am returned a trained model.

That's it. Can you do that?