5 citations · 10 across the 3 of their papers we have counts for
5 papers · 1 filter
Impact of Leakage on Data Harmonization in Machine Learning Pipelines in Class Imbalance Across Sites
Nicolás Nieto, Simon B. Eickhoff, Christian Jung +6
Machine learning (ML) models benefit from large datasets. Collecting data in biomedical domains is costly and challenging, hence, combining datasets has become a common practice. H…
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection
Jinyang Yu, Sami Hamdan, Leonard Sasse +2
Mutation validation (MV) is a recently proposed approach for model selection, garnering significant interest due to its unique characteristics and potential benefits compared to th…
On Leakage in Machine Learning Pipelines
Leonard Sasse, Eliana Nicolaisen-Sobesky, Juergen Dukart +9
Machine learning (ML) provides powerful tools for predictive modeling. ML's popularity stems from the promise of sample-level prediction with applications across a variety of field…
Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models
Sami Hamdan, Shammi More, Leonard Sasse +3
The fast-paced development of machine learning (ML) methods coupled with its increasing adoption in research poses challenges for researchers without extensive training in ML. In n…
Confound-leakage: Confound Removal in Machine Learning Leads to Leakage
Sami Hamdan, Bradley C. Love, Georg G. von Polier +4
Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine. To apply these approaches, confounds must first be remo…