A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning Tools
This work addresses the need for more intuitive AutoML tools to democratize machine learning for non-experts, but it is incremental as it builds on prior research by providing practical evaluations.
The paper evaluated three AutoML tools using a banking competition dataset to assess their performance and user experience for individuals with little machine learning background, finding that while they simplify model creation, significant improvements are needed to enhance usability for novices.
This paper discusses modern Auto Machine Learning (AutoML) tools from the perspective of a person with little prior experience in Machine Learning (ML). There are many AutoML tools both ready-to-use and under development, which are created to simplify and democratize usage of ML technologies in everyday life. Our position is that ML should be easy to use and available to a greater number of people. Prior research has identified the need for intuitive AutoML tools. This work seeks to understand how well AutoML tools have achieved that goal in practice. We evaluate three AutoML Tools to evaluate the end-user experience and system performance. We evaluate the tools by having them create models from a competition dataset on banking data. We report on their performance and the details of our experience. This process provides a unique understanding of the state of the art of AutoML tools. Finally, we use these experiences to inform a discussion on how future AutoML tools can improve the user experience for neophytes of Machine Learning.