Answer-first summary for fast verification
Answer: No
The solution does NOT meet the goal because it uses `run.log('AUC', auc)` without first obtaining the Azure ML run context. For Hyperdrive to optimize hyperparameters based on the AUC metric, the metric must be logged using the Azure ML run instance (e.g., `run = Run.get_context()` followed by `run.log('AUC', auc)`). The community discussion strongly supports this, with the top-voted comment (20 upvotes) emphasizing that logging with the Azure ML run context is required for Hyperdrive optimization. While the code correctly calculates AUC using `roc_auc_score`, the missing run context initialization means Hyperdrive cannot access the metric for hyperparameter tuning. Thus, the answer is 'No' (B).
Author: LeetQuiz Editorial Team
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You are using Azure Machine Learning to train a classification model and have configured HyperDrive to optimize the AUC metric. You plan to run a script that trains a random forest model, where the validation data labels are stored in a variable named y_test and the predicted probabilities are stored in a variable named y_predicted.
You need to add logging to the script to enable Hyperdrive to optimize hyperparameters for the AUC metric.
Proposed Solution: Run the following code:
from sklearn.metrics import roc_auc_score
auc = roc_auc_score(y_test, y_predicted)
run.log('AUC', auc)
from sklearn.metrics import roc_auc_score
auc = roc_auc_score(y_test, y_predicted)
run.log('AUC', auc)
Does the solution meet the goal?
A
Yes
B
No
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