Explanation:
The question requires configuring Azure ML's Automated ML to evaluate only linear models for a classification task. Option A directly addresses this by adding all non-linear algorithms to the blocked algorithms list, which restricts the AutoML process to linear models only. This is confirmed by the community consensus (100% agreement, highest upvotes) and aligns with Azure ML documentation on using blocked algorithms to exclude specific models. Other options are unsuitable: B (metric threshold) controls stopping criteria but not model type; C (disable featurization) affects feature engineering, not model selection; D (disable deep learning) excludes only deep learning models but not other non-linear algorithms; E (set to Regression) changes the task type, which contradicts the given classification requirement.
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You are using the Automated Machine Learning interface in Azure Machine Learning studio to train a classification model from a CSV file. You have set the task type to Classification.
You need to configure the Automated Machine Learning process to evaluate only linear models.
What should you do?
A
Add all algorithms other than linear ones to the blocked algorithms list.
B
Set the Exit criterion option to a metric score threshold.
C
Clear the option to perform automatic featurization.
D
Clear the option to enable deep learning.
E
Set the task type to Regression.
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