Abstract:
Automated Machine Learning is a research area which has gained a lot of focus in the recent past. But the various approaches followed by researchers and what has been disclosed by the available work is neither properly documented nor very clear due to the differences in the approaches. If the existing work is analyzed and brought under a common evaluation criterion, it will assist in continuing researches. This paper presents an analysis of the existing work in the domains of autoML, hyperparameter tuning and meta learning. The strongholds and drawbacks of the various approaches and their reviews in terms of algorithms supported, features and the implementations are explored. This paper is a results of the initial phase of an ongoing research, and in the future we hope to make use of this knowledge to create a design that will meet the gaps and the missing links identified.
