Impact of Dataset Size on Feature Selection Consistency and Accuracy of SageMaker Autopilot Versus H2O.ai and TPOT

Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. To understand how Auto-ML tools are used in practice today, we performed a qualitative study with participants ranging from novice hobbyists to industry researchers who use Auto-ML tools. We present insights into the benefits and deficiencies of existing tools, as well as the respective roles of the human and automation in ML workflows. Finally, we discuss design implications for the future of Auto-ML tool development

Research goal: What is the impact of dataset size on the feature selection consistency and accuracy of SageMaker Autopilot compared to other AutoML systems like H2O.ai and TPOT when evaluated across multiple domains?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.3/10.