Neyman-pearson classification: Parametrics and sample size requirement

Mathematics

• Classifier 100%
• Requirements 71%
• Sample Size 68%
• Type II error 48%
• Linear Discriminant Analysis 34%
• Thresholding 30%
• Type I error 28%
• Paradigm 25%
• Observation 24%
• Class 22%
• Spam 22%
• Random Forest 18%
• Binary Classification 17%
• Adaptive Strategies 17%
• Oracle Inequalities 17%
• Support Vector Machine 15%
• Scoring 15%
• Logistic Regression 13%
• Conditional probability 13%
• Excess 13%
• Parametric Model 13%
• Coding 12%
• Small Sample 12%
• High-dimensional 10%
• Upper bound 8%

Engineering & Materials Science

• Classifiers 62%
• Discriminant analysis 27%
• Logistic regression 14%
• Random forests 13%
• Support vector machines 10%