Adjusted R-squared
What is Adjusted R-squared?
In the artificial intelligence industry, adjusted R-squared is a crucial metric used to evaluate the performance of predictive models, particularly those involving multiple variables. Unlike the regular R-squared, which can be overly optimistic by increasing with the addition of more variables, adjusted R-squared provides a more accurate measure by penalizing the addition of less meaningful predictors. This makes it especially valuable when developing AI algorithms that aim to make predictions based on a large set of features. By considering the number of predictors, adjusted R-squared helps data scientists and AI engineers ensure their models are not just fitting the training data well but are also likely to perform well on unseen data, thereby improving the model's generalizability.
A statistical measure that indicates the goodness of fit of a regression model, adjusted for the number of predictors in the model.
Examples
- A fintech company uses adjusted R-squared to evaluate the performance of its credit scoring model, ensuring that additional financial indicators improve the model's predictive power without overfitting the data.
- An e-commerce platform employs adjusted R-squared to refine its recommendation engine, making sure that the inclusion of new user behavior metrics genuinely enhances the algorithm's ability to suggest relevant products.
Additional Information
- Adjusted R-squared can actually decrease if the added predictors do not improve the model, highlighting its robustness.
- It's often used alongside other metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for a comprehensive model evaluation.