  Many algorithms for data analysis exist, especially for classification
problems. To solve a data analysis problem, a proper algorithm should be
chosen, and also its hyperparameters should be selected. In this paper, we
present a new method for the simultaneous selection of an algorithm and its
hyperparameters. In order to do so, we reduced this problem to the multi-armed
bandit problem. We consider an algorithm as an arm and algorithm
hyperparameters search during a fixed time as the corresponding arm play. We
also suggest a problem-specific reward function. We performed the experiments
on 10 real datasets and compare the suggested method with the existing one
implemented in Auto-WEKA. The results show that our method is significantly
better in most of the cases and never worse than the Auto-WEKA.
