A machine learning system that competes with weka. It is an attempt to build a faster more stable and cleaner(as code) machine learning library for the scientists mass.
The Naive Bayes algorithms is one of the best performing algorithms for classification. It's strange that it wasn't implemented in the first iteration.
Currently we only have kmeans as a clustering algorithms. So we can't compare which is better for our data. We need to implement more clustering algorithms. We can look into the mahout implemented algorithms - Here;check the Perform Clustering subsection
The first idea was to use sonar, but as we currently don't have any hosting solution and most of us are with laptops - we should ditch that idea.
We will have to use the individual tools and run them through maven on every push to the repository.
Someone should read the git hooks documentation and do some tweaking so this happens automatically on push. Also a baseline for the rules should be decided - i.e. under what % of rule compliance we should have a warning.
Currently there is no way to evaluate the clustering algorithm heuristic. There should be some method similar to the one provided by WEKA. The idea is that the clusterer should group instances the same way they are grouped by the class attribute.
Currently we only support arff files which is inconvenient for most of the systems that want to build something with ML. We should support data importing from database