
Answer-first summary for fast verification
Answer: You should disable deep learning.
The question requires restricting Automated Machine Learning (AutoML) to assess only linear models for a classification task. Option A (disable deep learning) is correct because deep learning models are inherently non-linear and complex; disabling them ensures AutoML does not consider these non-linear algorithms. The community discussion strongly supports A with high upvotes (22, 5, 20) and reasoning that deep learning models are non-linear, and disabling them limits the scope to linear models like Logistic Regression or Linear SVM. Option B (enable automatic featurization) is incorrect as featurization handles data preprocessing (e.g., handling missing values, scaling) but does not restrict model algorithms; it may even enable non-linear features. Option C (disable automatic featurization) is incorrect because it prevents data transformations but does not block non-linear models. Option D (set task type to Forecasting) is irrelevant as the task is already specified as Classification. While some comments mention using 'blocked algorithms' for linear-only evaluation, this option is not provided, making A the best available choice based on the given options and community consensus.
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You are training a classification model using a CSV file with the Automated Machine Learning interface, configured for the Classification task type. You have been instructed that only linear models should be evaluated.
Which parameter must you configure to restrict the Automated Machine Learning run to assess only linear models?
A
You should disable deep learning.
B
You should enable automatic featurization.
C
You should disable automatic featurization.
D
You should set the task type to Forecasting.