Exam DP-100 topic 1 question 18 discussion

This is the answer.

SMOTE first selects a minority class instance a at random and finds its k nearest minority class neighbors. The synthetic instance is then created by choosing one of the k nearest neighbors b at random and connecting a and b to form a line segment in the feature space. The synthetic instances are generated as a convex combination of the two chosen instances a and b.

This article describes how to use the SMOTE component in Azure Machine Learning designer to increase the number of underrepresented cases in a dataset that's used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases. You connect the SMOTE component to a dataset that's imbalanced. There are many reasons why a dataset might be imbalanced. For example, the category you're targeting might be rare in the population, or the data might be difficult to collect. Typically, you use SMOTE when the class that you want to analyze is underrepresented. The component returns a dataset that contains the original samples. It also returns a number of synthetic minority samples, depending on the percentage that you specify. https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/smote

Correct.