AutoML Challenge: AutoML Framework Using Random Space Partitioning Optimizer

Automated machine learning provides a framework where an algorithm configuration best suited to a particular problem is automatically determined without users’ intervention. In this paper we present a method, referred to as Mondrian forests optimizer based on random space partitioning method, to modify the state-of-the-art system, auto-sklearn. We demonstrate that our method allows for incremental updating of a tree used for regression when the next candidate to be evaluated is given, while most of existing methods had to rebuild the tree. Our system, postech.mlg exbrain ranked the 4th place in Final3 and Final4 phases, and 3rd place in AutoML5 phase of AutoML Challenge.