Databricks Certified Machine Learning Associate · Free Practice Question Easy
Question 6
What are the standard evaluation metrics automatically computed for each run in an AutoML experiment when dealing with classification problems?
Choose only ONE best answer.
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A
All of these
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B
Accuracy
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C
Area Under the ROC Curve (AUC-ROC)
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D
Recall
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E
F1 Score
Reveal correct answer
Correct answer: A
Explanation
Correct Answer:
All of these
Explanation:
Why This Is Correct?
Databricks AutoML for classification automatically computes multiple standard metrics, including:
Accuracy: Overall correctness (
(TP+TN)/Total).AUC-ROC: Model’s ability to distinguish classes (higher = better).
Recall: True Positive Rate (
TP/(TP+FN)).F1 Score: Harmonic mean of precision and recall.
These metrics provide a holistic view of model performance across different thresholds and class imbalances.
Example Output:
- from databricks import automl
- summary = automl.classify(df, target_col="label")
- display(summary.trials) # Shows all metrics per trial
TrialAccuracyAUC-ROCRecallF110.920.980.910.93
Why Other Options Are Incorrect?
Individual metrics (Accuracy, AUC-ROC, Recall, F1) are part of the full set—not standalone.
Key Takeaway:
AutoML evaluates classification models using all key metrics by default. Use the leaderboard to compare trials and select the best model based on your priority (e.g., AUC for imbalanced data).
Pro Tip: Customize the primary_metric (e.g., "f1") if one metric matters most.
Discussion
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