How is the Adjusted R-Squared value computed, and why is it often preferred over R-Squared?
- Adjusted R-Squared adds a penalty for more predictors; preferred for its robustness to outliers
- Adjusted R-Squared considers bias; preferred for simplicity
- Adjusted R-Squared includes a penalty for more predictors; preferred for its consideration of model complexity
- Adjusted R-Squared includes mean error; preferred for interpretability
The Adjusted R-Squared value is computed by including a penalty term for the number of predictors in the model, unlike the regular R-Squared. This makes it often preferred over R-Squared, especially when dealing with multiple predictors, as it takes into consideration the complexity of the model. The adjustment ensures that only meaningful predictors enhance the model's performance, avoiding the tendency of R-Squared to increase with more variables.
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