by Victor
Last Updated September 12, 2019 04:19 AM

I encountered some statisticians that never use models other than Linear Regression for prediction because they believe that "ML models" such as random forest or gradient boosting are hard to explain or "not interpretable".

In a Linear Regression, given that the set of assumptions is verified (normality of errors, homoskedasticity, no multi-collinearity), the t-tests provide a way to test the significance of variables, tests that to my knowledge are not available in the random forests or gradient boosting models.

Therefore, my question is if I want to model a dependent variable with a set of independent variables, for the sake of interpretability should I always use Linear Regression?

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