Many different machine learning algorithms exist that can easily be used off the shelf, many of these methods are implemented in the open source WEKA package. However, each of these algorithms have their own hyperparameters that can drastically change their performance, and there are a staggeringly large number of possible alternatives overall.

Auto-WEKA considers the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous methods that address these issues in isolation. Auto-WEKA does this using a fully automated approach, leveraging recent innovations in Bayesian optimization.

This implementation interfaces Auto-WEKA to R by providing a package. It is partially based on the RWeka package.

RAutoWEKA depends on Auto-WEKA, hence, dependencies have to be resolved before development. Using the provided Makefile this should be done automatically:

$ cd java
$ make
$ cd ..