Foundations: is low R-squared a problem?

R-squared nowadays is a strange metric that is not even mentioned in the ML community anymore because it is not used to evaluate modern ML models. However, understanding the intuition behind it and its proper interpretation is helpful to understand how noise in the data can impact model parameters. This post is from a white paper I wrote several years ago but I think it is still a good piece to reflect on how noise in the data can impact model parameters.

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