Research Output for the Hybrid-AutoML System

Abstract

In this chapter, the authors use a set of use cases to evaluate how the hybrid autoML system is used to achieve the goals set out in the aims and objectives of this research. The authors map each use case to their aims and contributions as outlined in Section 1.3 of this research. A performance comparison is also made between autoWeka and the hybrid autoML system on 33 datasets. The comparison is carried out based on three main evaluation metrics such as the percentage accuracy (or correlation coefficient where applicable), the mean absolute error (MAE), and the time (in seconds) spent building the model on training data. It is observed that the hybrid autoML system fully outperforms autoWeka with regards to the time spent on building models or finding the best algorithms in the first instance.