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Answer: By using a Grouped Map Pandas UDF to train and apply models to each group individually.
The Grouped Map Pandas UDF is the appropriate choice for this scenario as it allows for the training and application of different models to different groups within the dataset. This API provides the flexibility needed to handle group-specific patterns and models effectively.
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Imagine you are working with a dataset where each group has a unique pattern that needs to be modeled separately. How would you use the Pandas Function API to train and apply these group-specific models?
A
By using a Scalar Pandas UDF to apply a single model to the entire dataset.
B
By using a Grouped Map Pandas UDF to train and apply models to each group individually.
C
By using an Iterator Pandas UDF to process the dataset in chunks and apply a single model.
D
By using a Grouped Aggregate Pandas UDF to aggregate data before applying models.
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