Title: Automatic Exploration of Machine Learning Experiments on OpenML,
Abstract: Understanding the influence of hyperparameters on the performance of a
machine learning algorithm is an important scientific topic in itself and can
help to improve automatic hyperparameter tuning procedures. Unfortunately,
experimental meta data for this purpose is still rare. This paper presents a
large, free and open dataset addressing this problem, containing results on 38
OpenML data sets, six different machine learning algorithms and many different
hyperparameter configurations. Results where generated by an automated random
sampling strategy, termed the OpenML Random Bot. Each algorithm was
cross-validated up to 20.000 times per dataset with different hyperparameters
settings, resulting in a meta dataset of around 2.5 million experiments
overall.