AutoWeka is an AutoML project that consists of many different machine learning algorithms. These algorithms and other existing functions in AutoWeka are easy to use. This open-source Weka package has algorithms, each of which has its own set of hyperparameters.

These hyperparameters can significantly change its performance. In addition, the package has many alternatives to hyperparameters for your use.

Auto-Weka takes into account the issue of simultaneously selecting a learning algorithm and assigning associated hyperparameters. To address these issues, Auto-Weka uses a fully automated approach and leverages the latest innovations that belong to Bayesian optimization. The framework seems fit for non-expert users who want to leverage the potential of machine learning for different applications.

Project Background

    • Library: AutoWeka AutoML Framework 
    • Author: Chris Thornton, Frank Hutter, Holger Hoos, Kevin Leyton-Brown
    • Initial Release: 2013
    • Type: For data prep, classification, regression, clustering, and visualization
    • License: GPL-3.0 License
    • Contains: Algorigthms, methods and hyperparameters
    • Language: Java, Python, Shell, HTML, CSS, Lex
    • GitHub: /dsibournemouth/autoweka
    • Runs On: Linux, Mac, Window


  • User modeling
  • Reinforcement learning
  • Classification
  • Regression 
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