In-Sample \(R^2\) is Not a Good Metric for Decision Making

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In many years of professional data science, I have seen many senior decision makers choose models based on in-sample \(R^2\) — the more the better. Many of them would not even tolerate a model with an \(R^2\) lower than \(0.8\) . These decision makers base their decisions on hyper inflated \(R^2\) models of the orders of millions of dollars (think about marketing spend). Here is a hard pill to…

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