How do I handle multicollinearity in my features?

How do I handle multicollinearity in my features?

Multicollinearity occurs when two or more features are highly correlated. You can handle it by removing one of the correlated features, using dimensionality reduction techniques like PCA, or applying regularization methods.

What are some signs that I have multicollinearity?

Signs of multicollinearity include high variance inflation factors (VIF), unstable coefficient estimates, and large standard errors for the coefficients.

Can you explain how to calculate the variance inflation factor?

Sure! The VIF for a feature is calculated as 1/(1-R²), where R² is the coefficient of determination obtained by regressing that feature against all other features.

What should I do if I find high VIF values?

If you find high VIF values, consider removing the feature with the highest VIF, combining features, or using regularization techniques like Lasso or Ridge regression.