Practice Key Metrics Derived From A Confusion Matrix (30.3)

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6 sample questions

Question 1.
What does accuracy measure in a classification model?
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Also recall that TP + TN contributes to this measure.
Question 2.
Define precision.
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Focus on the ratio of true positive predictions.
3Question 3.
What is the formula for accuracy?
- (TP + TN) / Total
- (TP) / (TP + FN)
- (TP) / (TP + FP)
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Think about what constitutes a correct prediction.
4Question 4.
True or False: Recall measures the proportion of true positives out of all predicted positives.
- True
- False
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Revisit the definitions of precision and recall.
Question 5.
Your model predicts 200 spam emails, 160 are truly spam and 40 are not. Calculate accuracy, precision, recall, and F1 score.
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Work through each metric step-by-step using the formulas.
Question 6.
You have a binary classification model with TP = 30, FP = 5, FN = 5, TN = 60. What can you say about its performance in terms of F1 Score? Is it suitable for healthcare application?
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Assess both precision and recall when discussing suitability.

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