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Answer: FeaturizationMode
The question asks which enum controls whether automated ML automatically handles missing value imputation and categorical feature encoding. FeaturizationMode is the correct choice because it specifically controls the featurization process, including these preprocessing steps. 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 not preprocessing, and RegressionPrimaryMetrics defines evaluation metrics rather than preprocessing behavior. The FeaturizationMode enum has values like AUTO, CUSTOM, and OFF that directly control whether automated featurization (including imputation and encoding) is performed.
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You plan to use automated machine learning with the Azure Machine Learning Python SDK v2 to train a regression model. Your dataset contains features with missing values and categorical features with a small number of distinct values.
You need to control whether automated machine learning automatically handles the imputation of missing values and the encoding of categorical features 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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