Neyman-pearson classification: Parametrics and sample size requirement

Mathematics

• Parametric 100%
• Type II error 75%
• Discriminant Analysis 50%
• Thresholding 50%
• Probability Theory 25%
• Upper Bound 25%
• Logistic Regression 25%
• Smaller Sample 25%
• Conditional Probability 25%
• Analysis Model 25%
• Parametric Model 25%
• Support Vector Machine 25%
• Distributional Assumption 25%
• Binary Classification 25%
• Classification Method 25%

Computer Science

• Sample Size Requirement 100%

Keyphrases

• Minimum Sample Size 40%
• 0-1 Coding 20%