A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention by Isabelle Guyon, Imad Chaabane, Hugo Jair Escalante, Sergio Escalera, Damir Jajetic, James Robert Lloyd, Nuria Macia, Bisakha Ray, Lukasz Romaszko, Michele Sebag, Alexander Statnikov, Sébastien Treguer, Evelyne Viegas
The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully automatic, black-box learning machines for feature-based classification and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranging across different types of complexity. Over five rounds, participants succeeded in delivering AutoML software capable of being trained and tested without human intervention. Although improvements can still be made to close the gap between human-tweaked and AutoML models, this challenge has been a leap forward in the field and its platform will remain available for post-challenge submissions at http://codalab.org/AutoML.Previously:
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