What are the best practices for handling missing data in supervised learning models?

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Missing data is a common and inevitable problem in many supervised learning models, especially when dealing with real-world datasets. It can affect the quality, accuracy, and performance of the models, and introduce bias and uncertainty in the results. Therefore, it is important to handle missing data properly and follow some best practices to avoid or minimize its negative impact. In this article, we will discuss some of the best practices for handling missing data in supervised learning models, such as identifying the types and patterns of missing data, choosing appropriate methods to deal with missing data, and evaluating the effects of missing data on the models.

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