Statistical Evaluation & Reporting Framework for Machine Learning Results

View the Project on GitHub cguckelsberger/statistical-evaluation-for-machine-learning

STATSREP-ML is an open-source solution for automating the process of eval- uating machine-learning results. It calculates qualitative statistics, performs the appropriate tests and reports them in a comprehensive way. It largely, but not exclusively, relies on well-tested and robust statistics implementations in R, and uses the tests the machine-learning community largely agreed upon.


Tests and Correction Methods

Additional tests can be easily integrated by means of calling the corresponding R packages or by implementing them natively in Java (See Wiki!).

Getting Started

Please have a look at our Wiki for a quick introduction to get you started, and more information on setting up and extending STATSREP-ML.

How to Cite?

If you use STATSREP-ML in research, please cite the following paper (Download):

Christian Guckelsberger, Axel Schulz (2014). STATSREP-ML: Statistical Evaluation & Reporting Framework for Machine Learning Results. Technical Report. Published by tuprints [].


While most STATSREP-ML modules are available under the Apache Software License (ASL) version 2, there are a few modules that depend on external libraries and are thus licensed under the GPL. The license of each individual module is specified in its LICENSE file.

It must be pointed out that while the component's source code itself is licensed under the ASL or GPL, individual components might make use of third-party libraries or products that are not licensed under the ASL or GPL. Please make sure that you are aware of the third party licenses and respect them.

This project was initiated under the auspices of Prof. Dr. Max Mühlhäuser, Telecooperation Lab (TK), Technische Universität Darmstadt.