A tool that helps users conduct supervised machine learning and acquire knowledge about that

<p>Growing demand for machine learning (ML) data analysis leads to the shortage of data scientists. Novice data scientists who do not have enough time to study ML data analysis beforehand sometimes engage in ML data analysis projects due to the shortage of data scientists. We have developed an open source Python library, MALSS to address the problem. MALSS helps users develop a machine learning model by automating the data analysis pipeline of the supervised learning. Furthermore, MALSS helps the users acquire knowledge about the ML data analysis by giving them an analysis report after the analysis. The report represents the process and results of the analysis and shows the common pitfalls in ML data analysis, which prevents automated data analysis from making the workflow of ML data analysis a black box. However, it is difficult for the analysis report to help ML data analysis in accordance with the input training data and the results of the subprocesses of the modeling processes. To handle the problem, we extend MALSS so that it can help ML data analysis in accordance with the training data and the results of the subprocesses. We confirmed that users using the proposed extended MALSS outperformed those using the conventional MALSS in an evaluational experiment and acquired basic knowledge about supervised ML data analysis the same as those using the conventional MALSS.</p>