Preventing dataset shift from breaking machine-learning biomarkers
Jéroôme Dockès,
Gaël Varoquaux,
Jean-Baptiste Poline
Abstract:Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine the application of the biomarker to new individuals. Dataset shifts are frequent in biomedical research, e.g. because of recruitment biases. When a dataset shif… Show more
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