Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction

This paper investigates the empirical relationship between predictive performance, often called predictive power, and interpretability of various Machine Learning algorithms, focusing on bicycle traffic data from four cities. As Machine Learning algorithms become increasingly embedded in decision-ma...

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Detalles Bibliográficos

Autores principales:	Fabian Obster, Monica I. Ciolacu, Andreas Humpe
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	IEEE 2024-01-01
Colección:	IEEE Access
Materias:	Explainable AI predictive analysis interpretability scoring empirical analysis bicycle traffic data bias-variance
Acceso en línea:	https://ieeexplore.ieee.org/document/10811902/
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