What is the Adjusted R-Squared, and how does it differ from the R-Squared?
- Less sensitive to errors
- More accurate in predicting future data
- More robust to outliers
- Takes into account the number of predictors
The Adjusted R-Squared differs from the regular R-Squared by taking into account the number of predictors in the model. While R-Squared will generally increase as more variables are added, regardless of their usefulness, the Adjusted R-Squared adjusts for this by penalizing the inclusion of irrelevant features. It's useful when comparing models with different numbers of predictors.
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