  The AutoML task consists of selecting the proper algorithm in a machine
learning portfolio, and its hyperparameter values, in order to deliver the best
performance on the dataset at hand. Mosaic, a Monte-Carlo tree search (MCTS)
based approach, is presented to handle the AutoML hybrid structural and
parametric expensive black-box optimization problem. Extensive empirical
studies are conducted to independently assess and compare: i) the optimization
processes based on Bayesian optimization or MCTS; ii) its warm-start
initialization; iii) the ensembling of the solutions gathered along the search.
Mosaic is assessed on the OpenML 100 benchmark and the Scikit-learn portfolio,
with statistically significant gains over Auto-Sklearn, winner of former
international AutoML challenges.
