Confusion Matrices Quiz

Answers and explanations appear as you take the quiz.

1. In a binary confusion matrix, what is a False Negative?
   • A positive case the model incorrectly predicted as negative
   • A negative correctly predicted as negative
   • A negative incorrectly predicted as positive
   • A positive correctly predicted as positive
2. Precision is calculated as:
   • TP / (TP + FN)
   • TP / (TP + FP)
   • TN / (TN + FP)
   • (TP + TN) / total
3. Which metric measures the proportion of actual positives that the model correctly identifies?
   • Precision
   • Specificity
   • Recall
   • Accuracy
4. In a confusion matrix for a problem with 5 classes, what do the diagonal cells represent?
   • Incorrect predictions
   • False positives only
   • The total number of samples
   • Correct predictions for each class
5. Why can accuracy alone be misleading without a confusion matrix?
   • It hides the balance between different error types like false positives and false negatives
   • Accuracy is always wrong
   • It cannot be computed for binary problems
   • It requires probabilities instead of labels
6. The F1 score is the harmonic mean of which two metrics?
   • Accuracy and specificity
   • Precision and recall
   • Sensitivity and specificity
   • True positives and true negatives
7. In a binary confusion matrix, which count is a False Positive?
   • An actual positive that the model predicts as positive
   • An actual positive that the model predicts as negative
   • An actual negative that the model predicts as positive
   • An actual negative that the model predicts as negative
8. Why should you check a library's confusion-matrix axis labels before calculating precision or recall?
   • The diagonal always contains false positives
   • Axis order changes the number of observations in the dataset
   • Precision and recall use no confusion-matrix cells
   • Libraries may place true classes in rows and predicted classes in columns, or use a different orientation