The effects of data leakage on connectome-based machine learning models.

ABSTRACT: Predictive modeling has now become a central technique in neuroimaging to identify complex brain-behavior relationships and test their generalizability to unseen data. However, data leakage, which unintentionally breaches the separation between data used to train and test the model, undermines the validity of predictive models. Previous literature suggests that leakage is generally pervasive in machine learning, but few studies have empirically evaluated the effects of leakage in neuroimaging data. Although leakage is always an incorrect practice, understanding the effects of leakage on neuroimaging predictive models provides insight into the extent to which leakage may affect the literature. Here, we investigated the effects of leakage on machine learning models in two common neuroimaging

SUBMITTER: Rosenblatt M 

PROVIDER: S-EPMC10793416 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature