Performance Evaluation of the Machine Learning Platform DataRobot

Abstract

In recent years, data analysis using AutoML has become popular. Since engineers in general are not necessarily familiar with machine learning for diagnosis of abnormalities and preventive maintenance of machines and structures, data analysis using AutoML has the potential to accelerate the introduction of machine learning technology in this field. While the machine learning platform DataRobot can implement algorithms with no code, it is necessary for humans to evaluate the balance between time cost and accuracy of the derived algorithms. In this study, we investigated the relationship between accuracy and processing time for each algorithm on binary classification, multinomial classification and regression, with the aim of optimizing the time cost and accuracy derived by DataRobot.