Navigating Reproducibility in Machine Learning

This chapter explores the challenges of achieving reproducibility in scientific research using machine learning, particularly in biomedical contexts like breast cancer studies. It examines the significance of statistical techniques and their impact on the generalizability of findings, stressing the importance of terminology and methodologies to enhance reproducibility. The discussion also includes the complexities of model validation and the role of post-selection inference, aiming to connect advanced machine learning methods with practical scientific applications.