MLS-C01 Modeling Practice Question
A data scientist is evaluating a classification model. The confusion matrix shows that the model has 50 true positives, 100 true negatives, 20 false positives, and 30 false negatives. Which TWO metrics can be calculated from this confusion matrix? (Choose two.)
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between metrics that are directly computed from the confusion matrix (like precision and recall) versus metrics that require additional calculations or are specific to regression tasks, leading candidates to mistakenly select F1 score as a direct metric or R-squared as applicable to classification.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Recall
Recall (also known as sensitivity) is calculated as TP / (TP + FN) = 50 / (50 + 30) = 0.625, measuring the proportion of actual positives correctly identified. Precision is calculated as TP / (TP + FP) = 50 / (50 + 20) = 0.714, measuring the proportion of positive predictions that are correct. Both metrics are directly derived from the four values in the confusion matrix.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
R-squared
Why it's wrong here
R-squared is for regression, not classification.
- ✗
F1 score
Why it's wrong here
F1 requires both precision and recall, but it is not directly from the confusion matrix without calculation. However, it is derivable. But since the question asks for 'TWO', precision and recall are more fundamental.
- ✓
Recall
Why this is correct
Recall = TP/(TP+FN) can be directly calculated.
- ✗
Root mean squared error
Why it's wrong here
RMSE is for regression.
- ✓
Precision
Why this is correct
Precision = TP/(TP+FP) can be directly calculated.
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