R-Squared

In Linear Regression is tells us how much of the total Variance of the target outcome is explained by the model.

$R^2=1-\frac{{\sum }_{i=1}^n{\left(y^{(i)}-{\hat{y}}^{(i)}\right)}^2}{{\sum }_{i=1}^n{\left(y^{(i)}-\bar{y}\right)}^2}$

• $<0$ → worse than mean predictor
• $0$ → mean predictor
• $1$ → best possible score.

Adjusted

Normal R-Squared gets bigger with more features, so it is better to use the adjusted version: ${\bar{R}}^2=1-\left(1-R^2\right)\frac{n-1}{n-p-1}$ where $n$ number of datapoints and $p$ number of features.