wekaMine packages the wide range of algorithms in the Weka machine learning library into a form that is easier to use and more suitable for real-world machine learning problems:
- Suite of scripts for command line interface using tab files instead of arff.
- wmModelSelection
- wmTrainModel
- wmClassify
- wmFilter
- wmGenFolds
- Standardized basic model selction, model creation, and model evaluation pipeline.
- Domain specific language to easily describe complex model selection experiments.
- Many additional algorithms
- BalancedRandomForest
- BimodalityIndexFilter
- MixtureModelFilter
- FisherLDEval
- Feature score outputs from CV folds.
- Wrappers to simplify using Weka as a Java library
- Groovy syntax additions to Weka classes (e.g. instances[featureName])
- Automatic support for instance IDs
- Whole trained pipeline encapsulated in a serialized model
- Feature selection (including finding appropriate feature intersections with new data)
- Trained classifier.
- Background model distribution.
Main page: http://jdurbin.github.io/wekaMine/
Documentaion on wiki here: https://github.com/jdurbin/wekaMine/wiki