Understanding Multicollinearity and Confounding Variables in Regression

Multicollinearity When two are more of the predictors are correlated, this phenomenon is called multicollinearity. This affects the resulting coefficients by masking the underlying individual weights of the correlated variables. This is why model weights are not equal to feature importance. Ways to deal with multicollinearity Looking at Variance Inflation Factor (VIf), which measures the … Continue reading Understanding Multicollinearity and Confounding Variables in Regression