Matthews Correlation Coefficient Quiz

Answers and explanations appear as you take the quiz.

1. A filter labels all 1,000 messages legitimate when 990 truly are legitimate. Why is its 99% accuracy insufficient evidence of useful spam detection?
   • Every majority-class error receives more weight than a spam error
   • Accuracy alone establishes that both classes have high recall
   • The filter has detected none of the ten spam messages
   • An MCC of 0.99 follows directly from this accuracy
2. Which value results from the guide's toy confusion matrix with 40 true positives, 40 true negatives and 10 of each error?
   • MCC of 0.6
   • MCC of 0.8
   • MCC of 0.4
   • MCC of 1.0
3. A report interprets an MCC of 0.6 as '60% of cases were correct.' How should that interpretation be corrected?
   • It means 60% of positive cases were found
   • It means 60% of predicted positives were correct
   • It means the model is calibrated at 60% confidence
   • MCC is a correlation measure, not the percentage classified correctly
4. For binary data containing both classes, a classifier reverses every label perfectly. Which MCC value describes that relationship?
   • 0
   • -1
   • 0.5
   • 1
5. A factory values missed defects differently from unnecessary inspections. Which additional evidence is needed alongside MCC?
   • A single pooled count of all mistakes, without their types
   • Only the accuracy at the current operating threshold
   • The error counts and their operational costs
   • Only a probability-calibration plot, without error counts
6. Which inputs are appropriate for scikit-learn's matthews_corrcoef in the workflow described?
   • Observed labels and predicted labels
   • Observed labels and unthresholded probability estimates
   • Predicted probabilities and their rounded copies
   • Training labels and labels from unrelated evaluation cases
7. Why does predicting a single class for every case require care when interpreting the binary MCC formula?
   • The score must equal the majority-class accuracy
   • The absent predicted class can be ignored without affecting the formula
   • Perfect recall for the chosen class makes the denominator one
   • One denominator factor becomes zero
8. When extending an MCC report to three classes, which approach matches the guide?
   • Compute three binary MCC values and take their unweighted mean
   • Use a multiclass MCC implementation and inspect the confusion matrix
   • Merge the two rarest classes before interpreting the original task
   • Use overall accuracy as the multiclass definition of MCC