Machine learning with a reject option: a survey

Hendrickx, Kilian; Perini, Lorenzo; Van der Plas, Dries; Meert, Wannes; Davis, Jesse

doi:10.1007/s10994-024-06534-x

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Book1

Preprint1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 8 publications

References 160 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Simple Heuristic for Controlling Human Workload in Learning to Defer

Ponomarev

2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A Simple Heuristic for Controlling Human Workload in Learning to Defer

Ponomarev

2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned

Davis,

Bransen,

Devos

et al. 2024

Mach Learn

View full text Add to dashboard Cite

There has been an explosion of data collected about sports. Because such data is extremely rich and complex, machine learning is increasingly being used to extract actionable insights from it. Typically, machine learning is used to build models and indicators that capture the skills, capabilities, and tendencies of athletes and teams. Such indicators and models are in turn used to inform decision-making at professional clubs. Designing these indicators requires paying careful attention to a number of subtle issues from a methodological and evaluation perspective. In this paper, we highlight these challenges in sports and discuss a variety of approaches for handling them. Methodologically, we highlight that dependencies affect how to perform data partitioning for evaluation as well as the need to consider contextual factors. From an evaluation perspective, we draw a distinction between evaluating the developed indicators themselves versus the underlying models that power them. We argue that both aspects must be considered, but that they require different approaches. We hope that this article helps bridge the gap between traditional sports expertise and modern data analytics by providing a structured framework with practical examples.

show abstract