<ul><li>Adjusted R-Squared is a modified version of standard R-Squared, used to evaluate the prediction accuracy of a regression model.</li><li>The difference with R-Squared is that adjusted R-squared considers the number of independent variables in the model.</li><li>The formula for adjusted R² is 1 - [(1 - R²) * (n - 1) / (n - k - 1)], where n is the number of observations and k is the number of predictors.</li><li>A higher adjusted R-squared value is generally considered better.</li></ul>

Adjusted R-squared in Machine Learning

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