Mark, the Student Risk Assessment Analyst at MDFT Pro, is evaluating the performance of a machine learning model that identifies students at risk of not completing their certification programs. MDFT Pro, a well-known training agency, uses this model to trigger early intervention programs and additional support services. Mark is particularly concerned about false negatives because these represent at-risk students who were incorrectly classified as successful, meaning they won’t receive the support they actually need to complete their programs successfully.
Based on the following confusion matrix for the student risk prediction model, how many false negative cases are there?
| Actual At-Risk | Actual Not At-Risk | |
|---|---|---|
| Predicted At-Risk | 11 | 5 |
| Predicted Not At-Risk | 1033 | 13951 |
How many false negative predictions does this confusion matrix show?
Choose the correct answer from the options below.
Explanations for each answer: