Neyman-Pearson Classification via Cost-Sensitive Learning

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Documentation for package ‘npcs’ version 0.1.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

cv.npcs	Compare the performance of the NPMC-CX, NPMC-ER, and vanilla models through cross-validation or bootstrapping methods
error_rate	Calculate the error rates for each class.
gamma_smote	Gamma-synthetic minority over-sampling technique (gamma-SMOTE).
generate_data	Generate the data.
npcs	Fit a multi-class Neyman-Pearson classifier with error controls via cost-sensitive learning.
predict.npcs	Predict new labels from new data based on the fitted NPMC classifier.
print.cv.npcs	Print the cv.npcs object.