In a multi-class classification problem with 5 classes, a data scientist in SAS wants to use a One-vs-Rest (OVR) strategy with PROC HPLOGISTIC. How many binary models must be trained?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because the One-vs-Rest strategy trains one binary classifier per class, where each classifier distinguishes that class from all other classes combined. With 5 classes, this requires 5 binary models.
Full explanation below image
Full Explanation
B is correct because the One-vs-Rest strategy trains one binary classifier per class, where each classifier distinguishes that class from all other classes combined. With 5 classes, this requires 5 binary models. A (10 models) describes the One-vs-One strategy, which trains one model per pair: C*(C-1)/2 = 10 models. C (4 models) is the number needed with a reference category in a single multinomial model. D is a different strategy — multinomial logistic regression — not OVR.