How can you model nonlinear and complex relationships without linear regression?
Linear regression is a powerful and widely used technique for modeling the relationship between a dependent variable and one or more independent variables. However, it has some limitations, such as assuming a linear and additive relationship, requiring normality and homoscedasticity of the residuals, and being sensitive to outliers and multicollinearity. In this article, you will learn how to model nonlinear and complex relationships without linear regression, using some alternative methods that can handle different types of data and patterns.
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Ramesh KadaliAccomplished Process Control Engineer | Experienced Leader | Advanced Data Analytics | Digital Technologies and IoT |…
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Rajive IslamManager Analytics, Reporting & Data Science @ CBA || Data Scientist || Data Analytics Mentor @ Academy Xi || Workshop…
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Torben Kirk WolfCo-founder @ KlimaID & Chief Consultant @ NIRAS