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Answer: Split the data into training and testing datasets.
Before building a classification model, it is essential to split the data into training and testing datasets. This allows you to train the model on one part of the data and test its performance on another, ensuring that the model generalizes well to new, unseen data. Therefore, the correct answer is C: Split the data into training and testing datasets.
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In the process of developing an Azure Machine Learning classification model for defective product identification, which action should be initiated first?
A
Load the dataset.
B
Create a clustering model.
C
Split the data into training and testing datasets.
D
Create a classification model.