MLS-C01 Modeling Practice Question
A data scientist is evaluating a binary classification model. The model's confusion matrix shows: True Positives=80, False Positives=20, True Negatives=900, False Negatives=0. Which THREE metrics can be calculated from this confusion matrix? (Choose three.)
⚠ Common exam trap
It's easy for candidates to assume AUC-ROC can be derived from a single confusion matrix, but it actually requires the full distribution of predicted probabilities to plot the ROC curve and calculate the area under it.
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
✓
Precision
Precision is calculated as TP/(TP+FP) = 80/(80+20) = 0.80. This metric measures the proportion of positive identifications that were actually correct, which is directly derivable from the confusion matrix values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Precision
Why this is correct
Precision = TP/(TP+FP).
- ✓
Recall
Why this is correct
Recall = TP/(TP+FN).
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC requires predicted scores, not just the confusion matrix.
- ✓
Accuracy
Why this is correct
Accuracy can be computed from TP, TN, FP, FN.
- ✗
Root Mean Squared Error (RMSE)
Why it's wrong here
RMSE is for regression tasks.
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