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Answer: FeaturizationMode
The question asks which enum controls whether automated machine learning automatically handles missing value imputation and categorical feature encoding. FeaturizationMode is the correct choice because it specifically controls the featurization process, including handling missing values and encoding categorical features. The community discussion confirms this with 100% consensus on option C, with detailed explanations noting that ForecastHorizonMode is for time series, RegressionModels specifies which models to use but doesn't control featurization, and RegressionPrimaryMetrics defines evaluation metrics rather than preprocessing steps. The FeaturizationMode enum has values like AUTO, CUSTOM, and OFF that directly control these preprocessing operations.
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You are using Azure Machine Learning Python SDK v2 to train a regression model with automated machine learning. Your dataset contains features with missing values and categorical features with few distinct values.
You need to manage whether automated machine learning automatically handles missing value imputation and categorical feature encoding during the training task.
Which enum from the automl package should you use?
A
ForecastHorizonMode
B
RegressionModels
C
FeaturizationMode
D
RegressionPrimaryMetrics
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