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Why we prefer VIF if we can find multicollinearity from the correlation matrix as well? What is the exact logic behind it?

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The correlation matrix is not a reliable measurement for multicollinearity because it only considers the pairwise effects. Unfortunately, multicollinearity is defined as:

Phenomenon in which two or more predictor variables in a multiple regression model are highly correlated,

Do you see the point? You'll need to consider the correlation with all other variables in your data set, not just 1-to-1 pairwise comparison.

VIF addresses the issue.

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If you have 10000 rows and 100 columns which have all 100 1s and the rest 0s. Every row has a 1 and the rest 0, a classic one hot encoded matrix. The correlations between two random columns is -0.01010101, which means the correlation matrix has a diagonal of 1s and the rest is -0.01010101. However this matrix is perfectly multicolinear.

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