Supporting the Design of Machine Learning Workflows with a Recommendation System

Abstract

Machine learning and data analytics tasks in practice require several consecutive processing steps. RapidMiner is a widely used software tool for the development and execution of such analytics workflows. Unlike many other algorithm toolkits, it comprises a visual editor that allows the user to design processes on a conceptual level. This conceptual and visual approach helps the user to abstract from the technical details during the development phase and to retain a focus on the core modeling task. The large set of preimplemented data analysis and machine learning operations available in the tool, as well as their logical dependencies, can, however, be overwhelming in particular for novice users. In this work, we present an add-on to the RapidMiner framework that supports the user during the modeling phase by recommending additional operations to insert into the currently developed machine learning workflow. First, we propose different recommendation techniques and evaluate them in an offline setting using a pool of several thousand existing workflows. Second, we present the results of a laboratory study, which show that our tool helps users to significantly increase the efficiency of the modeling process. Finally, we report on analyses using data that were collected during the real-world deployment of the plug-in component and compare the results of the live deployment of the tool with the results obtained through an offline analysis and a replay simulation.