Adjusted R-Squared

Adjusted R-Squared is a statistical measure used to evaluate the goodness of fit of a regression model. Unlike the regular R-Squared, which can be artificially inflated by adding more predictors, Adjusted R-Squared accounts for the number of predictors in the model relative to the number of observations. This makes it a more reliable metric for comparing models with different numbers of predictors. It is commonly used in data science and machine learning to assess model performance, particularly in linear regression contexts, helping practitioners to avoid overfitting while selecting the most appropriate model.