Comparing interpretability and explainability for feature selection

no code implementations • 11 May 2021 • Jack Dunn, Luca Mingardi, Ying Daisy Zhuo

In this paper, we investigate the performance of variable importance as a feature selection method across various black-box and interpretable machine learning methods.

BIG-bench Machine Learning feature selection +1

From predictions to prescriptions: A data-driven response to COVID-19

no code implementations • 30 Jun 2020 • Dimitris Bertsimas, Léonard Boussioux, Ryan Cory Wright, Arthur Delarue, Vassilis Digalakis Jr., Alexandre Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg, Cynthia Zeng

Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact.

Descriptive Management