68 citations · 73 across the 3 of their papers we have counts for
3 papers · 1 filter
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…