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Answer: Divide the dataset into training and testing subsets, evaluating with the testing subset.
In machine learning, it's a standard practice to split the original dataset into two subsets: one for training and one for testing. This approach helps to ensure that the model's performance can be fairly evaluated by testing it on data that it has never seen before. Option A correctly suggests dividing the dataset into training and testing subsets and then using the testing subset for evaluation. This method helps to validate the model's generalization ability. Options B, C, and D do not follow this best practice for model evaluation.
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In Azure Machine Learning designer, how should you proceed to develop and assess a clustering model?
A
Divide the dataset into training and testing subsets, evaluating with the testing subset.
B
Utilize the complete dataset for both training and evaluation.
C
Separate the dataset into feature and label subsets, evaluating with the feature subset.
D
Create training and testing subsets from the dataset, evaluating with the training subset.