Confusion Matrix and ROC Curves Quiz

Quiz covering Model Evaluation and Selection

Questions in this quiz

A preview of the 5 questions covered. Start the quiz above to answer them, check your score, and read the explanations.

1. 1

   In a binary classification confusion matrix, what does the 'False Negative' (FN) value represent?

2. 2

   You are evaluating a credit card fraud detection model on Azure. Since fraud is rare, the dataset is highly imbalanced. Which metric should you prioritize to assess performance?

3. 3

   What does the Area Under the ROC Curve (AUC-ROC) score of 0.5 indicate?

4. 4

   If you increase the probability threshold for a binary classifier, how does it typically affect the Precision and Recall?

5. 5

   When comparing two models using ROC curves, Model A has a higher AUC than Model B, but Model B has a higher True Positive Rate at a very low False Positive Rate. Which model should you choose if your business requirement is to ensure almost zero false alarms, even if it means missing some positive cases?