How can you address collinearity in your data?
Collinearity is a common problem in data science that can affect the performance and interpretation of your machine learning models. It occurs when two or more features in your data are highly correlated, meaning they share similar information or patterns. This can lead to issues such as inflated variance, unstable coefficients, and misleading significance tests. In this article, you will learn how to detect and address collinearity in your data using different methods and tools.
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Paresh PatilLinkedIn Top Data Science Voice💡| 5X LinkedIn Top Voice | ML, Deep Learning & Python Expert, Data Scientist | Data…
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Umaid AsimCEO at SensViz | Building human-centric AI applications that truly understands and empowers you | Helping businesses…
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Md Hasan ShahriarData Scientist | Machine Learning Engineer | MS Grad @Universität Potsdam