In the student retention case study, support vector machines used in prediction had proportionally more true positives than true negatives.

Question

Quizplus · Accepted Answer

This statement is true. The support vector machines used in prediction had a higher rate of correctly identifying students who would drop out (true positives) than correctly identifying students who would not drop out (true negatives). This is because the model was designed to prioritize identifying students at high risk of dropping out, in order to target interventions towards them.

In the Student Retention Case Study, Support Vector Machines Used