Towards open machine learning: Mloss.org and mldata.org

Abstract

Machine Learning (ML) is a scientific field comprised of both theoretical and empirical results. For methodological advances, one key aspect of reproducible research is the ability to compare a proposed approach with the current state of the art. Such a comparison can be theoretical in nature, but often a detailed theoretical analysis is not possible or may not tell the whole story. In such cases, an empirical comparison is necessary. To produce reproducible machine learning research, there are three main required components that need to be easily available: - The paper describing the method clearly and comprehensively. - The data on which the results are computed. - Software (possibly source code) that implements the method and produces the figures and tables of results in the paper. We share our experiences about mloss.org and mldata.org, community efforts towards encouraging open source software and open data in machine learning.