MLA-C01 ML Model Development Practice Question

A data scientist is evaluating a binary classification model. They have the confusion matrix and want to assess the model's performance comprehensively. Which THREE metrics should they consider? (Select THREE.)

⚠ Common exam trap

MLA-C01 often tests the confusion between classification and regression metrics, so candidates may incorrectly select RMSE or R² for a classification task.

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 (A) is correct because it measures the proportion of positive predictions that are actually correct (TP / (TP + FP)), which is essential for evaluating a binary classifier's reliability on predicted positives. Recall (C) is correct because it measures the proportion of actual positives that were correctly identified (TP / (TP + FN)), capturing the model's ability to find all relevant cases. F1 score (D) is correct because it is the harmonic mean of precision and recall (2 × (Precision × Recall) / (Precision + Recall)), giving a balanced single metric when both false positives and false negatives matter. RMSE (B) is not appropriate here because it is a regression error metric measuring the square root of the average squared difference between predicted and actual continuous values, not classification outcomes. R² (E) is also a regression metric that quantifies the proportion of variance explained in a continuous target, so it does not apply to a binary classification 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.

✓
Precision
Why this is correct
Precision measures the proportion of positive predictions that are actually correct, derived from the confusion matrix's true-positive and false-positive counts. It is one of the three complementary metrics needed to assess a binary classifier comprehensively.
✗
RMSE
Why it's wrong here
RMSE measures the average magnitude of error between predicted and actual continuous values, which a confusion matrix's four counts cannot produce. It is tempting because RMSE is a standard regression metric, and would be correct when evaluating a model that predicts numeric quantities rather than class labels.
✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified, calculated from true positives and false negatives in the confusion matrix. It complements precision by exposing missed detections, forming part of a comprehensive binary classification evaluation.
✓
F1 score
Why this is correct
F1 score is the harmonic mean of precision and recall, so it captures both false positives and false negatives in a single value. This satisfies the stem's requirement for comprehensive binary classification assessment alongside the confusion matrix, particularly valuable when class distribution is uneven.
✗
R²
Why it's wrong here
R² expresses the proportion of variance in a continuous target explained by the model, requiring numeric predictions and residuals that a confusion matrix does not contain. It is tempting because R² summarises overall fit, and would be correct for regression models rather than binary classification.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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