Sources of Irreproducibility in Machine Learning: A Review

no code implementations • 15 Apr 2022 • Odd Erik Gundersen, Kevin Coakley, Christine Kirkpatrick, Yolanda Gil

Objective: The objective of this paper is to develop a framework that enable applied data science practitioners and researchers to understand which experiment design choices can lead to false findings and how and by this help in analyzing the conclusions of reproducibility experiments.

Attribute BIG-bench Machine Learning +1