Hello everyone, I am currently experimenting with Ignite machine learning (random forest regression / classifier) and have come up with a couple of questions that I can't seem to answer using docs or sample code. I am rather new to ML as well as Ignite, so I hope that answers aren't too obvious. ;-)
Is my assumption correct that the label is the coordinate that is supposed to be learned (possibly depending on all other features) and later predicted by the model? At the moment, I am training my model from a local cache (CacheMode.LOCAL) that I populate through a CacheStoreAdapter from ElasticSearch as I can fit all data into RAM of a single node. Training seems to be single-threaded, though. Is there a way to parallelize the training across available cores while still limiting the operation to a single JVM process? After training a model I'd like to figure out the importance of the different features. Is there a way to obtain the feature importance from the model? Thanks, Thilo
