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wekaMine

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