Variance Inflation Factor (VIF) is a metric used to assess the extent of multicollinearity in a multiple linear regression model. It quantifies how much the variance of an estimated regression coefficient increases due to collinearity with other predictors. High VIF values indicate that the predictor variables are highly correlated with each other, which can lead to unreliable coefficient estimates and make it difficult to determine the individual effect of each predictor on the response variable.
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