$R^2$ is used in order to understand the amount of variability in the data that is explained by your model. A $R^2$ of $90%$ means that the $90%$ of the variance of the data is explained by the model, that is a good value. On practice you cannot rely only on the $R^2$, but is a type of measure that you can find.

The Chi-Square goodness of feat instead determines if your data matches a population, is a test in order to understand what kind of distribution follow your data. Instead, the Chi Square statistic is commonly used for testing relationships between categorical variables.

There are other posts in this forum that explain this difference, and there are many sites that explain these two variable.

$R^2$ is used in order to understand the amount of variability in the data that is explained by your model. A $R^2$ of $90%$ means that the $90%$ of the variance of the data is explained by the model, that is a good value. On practice you cannot rely only on the $R^2$, but is a type of measure that you can find.

The Chi-Square goodness of feat instead determines if your data matches a population, is a test in order to understand what kind of distribution follow your data. Instead, the Chi Square statistic is commonly used for testing relationships between categorical variables.

There are other posts in this forum that explain this difference, and there are many sites that explain these two variable.