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Answer: Train, Score, Evaluate.
The correct sequence in Azure Machine Learning designer for evaluating a classification model's AUC is: Train (to train the model on the training data), Score (to generate predictions on the test data), and Evaluate (to compute performance metrics including AUC). This follows the standard machine learning workflow where training precedes scoring, and scoring precedes evaluation. Option A (Train, Score, Evaluate) aligns with this workflow. Option B (Score, Evaluate, Train) is incorrect because scoring and evaluating require a trained model. Option C (Evaluate, Export Data, Train) is illogical as evaluation cannot occur before training. Option D (Train, Score, Export Data) omits the critical Evaluate module needed to compute AUC. The community discussion strongly supports Option A with 100% consensus and upvoted comments explaining the correct module sequence.
Author: LeetQuiz Editorial Team
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You are using Azure Machine Learning designer to build an experiment. After splitting a dataset into training and testing sets, you configure the algorithm as a Two-Class Boosted Decision Tree. You need to determine the Area Under the Curve (AUC). Which module combination should you use in sequence to accomplish this goal?
A
Train, Score, Evaluate.
B
Score, Evaluate, Train.
C
Evaluate, Export Data, Train.
D
Train, Score, Export Data.
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