Adjusted R-squared value is the modification of R-squared Value. In R-squared Value value, it always increases with adding a parameter in the Linear Regression. But it doesn't always mean that it has added a lot of value to the prediction. On the other hand, adjusted R-squared value increases only when the parameter is significant.

• Adjusted R-squared value helps to balance between accuracy and model's complexity as it penalizes predictor with high complexity.
• It decreases overfitting by penalizing very complex models
• A good metric to compare multiple predictors, as R-squared Value will always increase with the complexity of the model. So a very complex model with always get higher R-squared Value than a less complex but more accurate and robust model.

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