68 citations · 85 across the 7 of their papers we have counts for
8 papers
The impact of MRI image quality on statistical and predictive analysis on voxel based morphology
Felix Hoffstaedter, Nicolás Nieto, Simon B. Eickhoff +1
Image Quality of MRI brain scans is strongly influenced by within scanner head movements and the resulting image artifacts alter derived measures like brain volume and cortical thi…
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…
Predictive Data Calibration for Linear Correlation Significance Testing
Kaustubh R. Patil, Simon B. Eickhoff, Robert Langner
Inferring linear relationships lies at the heart of many empirical investigations. A measure of linear dependence should correctly evaluate the strength of the relationship as well…
Reviving Threshold-Moving: a Simple Plug-in Bagging Ensemble for Binary and Multiclass Imbalanced Data
Guillem Collell, Drazen Prelec, Kaustubh Patil
Class imbalance presents a major hurdle in the application of data mining methods. A common practice to deal with it is to create ensembles of classifiers that learn from resampled…