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Full-Text Articles in Statistical Models
Stressor: An R Package For Benchmarking Machine Learning Models, Samuel A. Haycock
Stressor: An R Package For Benchmarking Machine Learning Models, Samuel A. Haycock
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
Many discipline specific researchers need a way to quickly compare the accuracy of their predictive models to other alternatives. However, many of these researchers are not experienced with multiple programming languages. Python has recently been the leader in machine learning functionality, which includes the PyCaret library that allows users to develop high-performing machine learning models with only a few lines of code. The goal of the stressor package is to help users of the R programming language access the advantages of PyCaret without having to learn Python. This allows the user to leverage R’s powerful data analysis workflows, while simultaneously …
Evaluating Models Of Scanpath Prediction, Matthias Kümmerer, Matthias Bethge
Evaluating Models Of Scanpath Prediction, Matthias Kümmerer, Matthias Bethge
MODVIS Workshop
No abstract provided.
Consistent Saliency Benchmarking: How One Model Can Win On All Metrics, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge
Consistent Saliency Benchmarking: How One Model Can Win On All Metrics, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge
MODVIS Workshop
No abstract provided.