Search Results for author:
Found 4 papers, 2 papers with code
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection
Mutation validation (MV) is a recently proposed approach for model selection, garnering significant interest due to its unique characteristics and potential benefits compared to the widely used cross-validation (CV) method.
On Leakage in Machine Learning Pipelines
Machine learning (ML) provides powerful tools for predictive modeling.
Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models
We created julearn, an open-source Python library, that allow researchers to design and evaluate complex ML pipelines without encountering in common pitfalls.
Confound-leakage: Confound Removal in Machine Learning Leads to Leakage
Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
