The Random Forest algorithm is much described elsewhere, but in short it is a very good choice for prediction problems that involves ensemble learning (aggregating a combination of several models to solve a prediction problem). It is very easy to setup and use, and can be great for classification.
I was inspired by Pascal van Kooten’s whereami package (which was actually inspired by FIND!), to implement Random Forests as one of the machine learning algorithms available.
Random forests have been implemented as a separate Python TCP server that uses
sklearn routines to generate learning models and classify the fingerprints. So far it works very well and I’m impressed with the results!