Handling Multicollinearity in Feature Selection
Автор: NextGen AI Explorer
Загружено: 2025-07-26
Просмотров: 58
Описание: Multicollinearity occurs when two or more predictors in a linear regression model are highly correlated, leading to unreliable coefficient estimates. This segment covers strategies for detecting multicollinearity using Variance Inflation Factor (VIF) and correlation matrices. We will explore feature selection techniques that mitigate multicollinearity's impact, such as Lasso regression and principal component analysis (PCA). Through a practical coding demonstration, you'll learn how to identify and address multicollinearity, ensuring your linear regression model remains robust and interpretable.
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![How To Handle Multicollinearity and Feature Selection [DoorDash Data Science Project]](https://imager.clipsaver.ru/gh5JzALBQvU/max.jpg)
