Coming from a social sciences background, I learned to use R-squared as a way to assess model performance and goodness of fit for regression models.

Yet, in my current day job, I nearly never use the metric any more. I tend to focus on predictive power, with metrics such as MAE, MSE, or RMSE. These make much more sense to me when comparing models and their business value, and are easier to explain to stakeholders as an added bonus.

I recently wrote about the predictive power score as an alternative to correlation analysis.

Are there similar alternatives that render R-squared useless? And why?

Here’s **an interesting blog** explaining the standpoints of Cosma Shalizi of Carnegie Mellon University:

- R-squared does not measure goodness of fit.
- R-squared does not measure predictive error.
- R-squared does not allow you to compare models using transformed responses.
- R-squared does not measure how one variable explains another.

I have never found a situation where R-squared helped at all.

Professor Cosma Shalizi (according to Clay Ford)