When perfect is a warning: explanations expose shortcuts that accuracy hides in cancer gene-expression classifiers
When the classes in a training set come from different data sources, a classifier can score near-perfectly while learning where a sample came from. We show this for cancer classifiers trained on tumours from The Cancer Genome Atlas and normal tissue from the Genotype Tissue Expression project. Six model families, including a tabular foundation model, reached an area under the curve of 1.000 yet…
We haven't written up this one. bioRxiv has the full story — the link below goes straight to it.
