Hyperparameters Search Methods for Machine Learning Linear Workflows
Klára Pešková, Roman Neruda · 2019
Hyperparameters optimization is one of the most important metalearning features that is used in AutoML systems. In this paper we use hyperparameters-space search algorithms to optimize the settings of supervised machine learning methods and workflows. We focus on changes in performance of hyperparameters optimization algorithms with the growing complexity of the hyperparameters-space, when using the data preprocessings adds more parameters to the configuration and thus more dimensions to the searched space.