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Checking for multicollinearity using VIF

Checking for multicollinearity using VIF - SQL Tutorial

From the course: Complete Guide to AI and Data Science for SQL Developers: From Beginner to Advanced

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Checking for multicollinearity using VIF

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- [Instructor] So, let's get right into it. As you dive into your 13th step here, let's revisit the concept of multicollinearity. Now, if you recall, multicollinearity simply means that in your dataset, some independent variables are very similar to each other. They move together like two peas in a pod. Now, why does this matter? Well, it can make your linear regression model a bit confused, leading to results that aren't so reliable. So we're checking for multicollinearity to make sure your data plays nicely with your model. But how do we check for it? Enter our trusty tool, the variance inflation factor, or VIF for short. VIF is like a detective tool that checks if some of your variables are too close, like best friends who always show up to the party together. It helps you spot when these variables are so cozy that they mess up your model's predictions. We don't want that because it makes your model unreliable. So VIF…

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