AutoML-Med uses Latin Hypercube Sampling and Partial Rank Correlation Coefficient to automatically find preprocessing and model configurations that improve balanced accuracy and sensitivity on imbalanced medical tabular datasets.
Why do tree-based models still outperform deep learning on tabular data?
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AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data
AutoML-Med uses Latin Hypercube Sampling and Partial Rank Correlation Coefficient to automatically find preprocessing and model configurations that improve balanced accuracy and sensitivity on imbalanced medical tabular datasets.