Ensemble Diversity in SageMaker Autopilot: Robustness and Accuracy Analysis

Abstract Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of classification. Random Forest has emerged as a quite useful algorithm that can handle the feature selection issue even with a higher number of variables. In this paper, we use three popular datasets with a higher number of variables (Bank Marketing, Car Evaluation Database, Human Activity Recognition Using Smartphones) to conduct the experiment. There are four main reasons why feature selection

Research goal: To what extent does the ensemble diversity in SageMaker Autopilot affect its robustness and accuracy compared to single-model AutoML solutions like H2O.ai and TPOT on the Amazon Employee Access dataset?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.