Adjusted R-Squared
Machine Learning
A modified version of R-squared that adjusts for the number of predictors in the model.
Detailed Explanation
Adjusted R-Squared is a statistical measure used in machine learning to evaluate a model's performance, accounting for the number of predictors. Unlike R-squared, it penalizes the addition of unnecessary variables, preventing overfitting. It provides a more accurate assessment of how well the model explains variance in the data, especially when comparing models with different numbers of features.
Use Cases
- •Use case: Comparing multiple regression models to select the one that balances complexity and predictive accuracy.
Related Terms
Other terms in the Machine Learning category