68 citations · 73 across the 3 of their papers we have counts for
4 papers
Region-wise stacking ensembles for estimating brain-age using MRI
Georgios Antonopoulos, Shammi More, Simon B. Eickhoff +2
Predictive modeling using structural magnetic resonance imaging (MRI) data is a prominent approach to study brain-aging. Machine learning algorithms and feature extraction methods…
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…
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…