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Found 2 papers, 2 papers with code
Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification
In contrast, the Neyman-Pearson paradigm can train classifiers to achieve a high-probability control of the population type I error, but it relies on sample splitting that reduces the effective training sample size.
Issues arising from benchmarking single-cell RNA sequencing imputation methods
We find that the semi-synthetic data have very different properties from those of real scRNA-seq data and that the cell clusters used for benchmarking are inconsistent with the cell types labeled by biologists.
Applications Genomics Quantitative Methods
