IDENTIFYING OUTLIERS IN POPULATION COHORTS: A BENCHMARK SCORE APPROACH WITH ML
Keywords:
Outlier Detection, ML Hyperparameter TuningAbstract
This paper presents a novel framework for outlier detection based on group-specific predictions. A machine learning model is trained, and predictions are made for test groups using control and no-control techniques. Group predictions are compared to actual outcomes, and significant discrepancies are flagged as outliers. The framework leverages ML Hyperparameter tuning techniques to optimize model performance, enabling robust and automated detection of outlier groups.
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Copyright (c) 2024 Vibhu Verma (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
