How to evaluate regression models? explain r squared and adjusted r squared and difference between them

Question

Accepted Answer

Regression models can be evaluated using R squared and adjusted R squared to measure the goodness of fit.

R squared measures the proportion of the variance in the dependent variable that is predictable from the independent variables.
Adjusted R squared adjusts for the number of predictors in the model, providing a more accurate measure of goodness of fit.
R squared can be artificially inflated by adding more predictors, while adjusted R squared penalizes for adding unnecessary variables.
R squared ranges from 0 to 1, where 1 indicates a perfect fit, while adjusted R squared can be negative if the model is worse than a simple average.
A higher R squared or adjusted R squared value indicates a better fit of the regression model.

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