What are the flaws in R-squared?

Data Science, AI and ML
#1

here are two major flaws of R-squared:

Problem- 1: As we are adding more and more predictors, R² always increases irrespective of the impact of the predictor on the model. As R² always increases and never decreases, it can always appear to be a better fit with the more independent variables(predictors) we add to the model. This can be completely misleading.

Problem- 2: Similarly, if our model has too many independent variables and too many high-order polynomials, we can also face the problem of over-fitting the data. Whenever the data is over-fitted, it can lead to a misleadingly high R² value which eventually can lead to misleading predictions.