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CW 483
Hendrik Blockeel and Joaquin Vanschoren
Experiment databases: Towards an improved experimental methodology in machine learning
Abstract
Machine learning research often has a large experimental component. While the experimental methodology employed in machine learning has improved much over the years, repeatability of experiments and generalizability of results remain a concern. In this paper we propose a methodology based on the use of experiment databases. Experiment databases stimulate large-scale experimentation, guarantee repeatability of experiments, improve reusability of experiments, help explicitating the conditions under which certain results are valid, and support quick hypothesis testing as well as hypothesis generation. We show that they have the potential to significantly increase the ease with which new results in machine learning are obtained and correctly interpreted.
report.pdf (1.8M) / mailto: H. Blockeel