Does the solution meet the goal?

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

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You are creating a new experiment in Azure Learning learning Studio.

One class has a much smaller number of observations than the other classes in the training

You need to select an appropriate data sampling strategy to compensate for the class imbalance.

Solution: You use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

Does the solution meet the goal?
A . Yes
B . No

View Answer

Answer: A

Explanation:

SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote