SageMaker Autopilot Accuracy and Feature Engineering Runtime Trade-offs Across Varying Tabular Dataset Sizes and Complexities

Epilepsy is a neurological disease characterized by recurrent seizures caused by abnormal electrical activity in the brain. One of the methods used to diagnose epilepsy is through electroencephalogram (EEG) analysis. EEG is a non-invasive medical test for quantifying electrical activity in the brain. Applying machine learning (ML) to EEG data for epilepsy diagnosis has the potential to be more accurate and efficient. However, expert knowledge is required to set up the ML model with correct hyperparameters. Automated machine learning (AutoML) tools aim to make ML more accessible to non-experts

Research goal: What is the impact of varying the size and complexity of tabular datasets (e.g., from OpenML-CC18) on the trade-off between model accuracy and feature engineering runtime in SageMaker Autopilot, compared to static feature selection methods?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.8/10.