Difference between R² and Chi-Square The question is in the title: In both cases I use R² and Chi-Square to test if a fit/model is good enough. Up to now I only know that R² is used for models (?) and Chi-Square for Fits/functions (?). Is this true? And how exactly do they differ? 
Thank you!
 A: Found this after a quick google: "R^2 is used to quantify the amount of variability in the data that is explained by your model. It's useful for comparing the fits of different models.
The Chi-square goodness of fit test is used to test if your data follows a particular distribution. It's more useful for testing model assumptions rather than comparing models."
sounds like Chi-square is more useful if you have a function you are trying to test (or a model you are trying to fit to your data) as opposed to the R^2 which tells you how much variability there is in your data, and therefore how much the best model fits.
A: Chi^2 provides a per-feature measurement of dependency with the target. This is useful at the feature-selection stage, for a classification model. We'd like to weed out the low-dependent features.
(scikit-learn guide for additional such measurements for classification and regression models).
R^2 provides a model-level measurement of the target's variance explained. This is useful at the model-evaluation stage. (scikit-learn guide for R^2)
