R Squared Vs Adjusted R Squared: Explaining The Key Differences

As far as regression models are concerned, there is a certain degree of level of correlation between the independent and dependent variables in the dataset that let us predict the dependent variable. In statistics, this correlation can be explained using R Squared and Adjusted R Squared. In other words, R Squared and Adjusted R Squared help us determine how much of the variation in the value of a dependent variable (y)  is explained by the values of the independent variable(s) (X, X1, X, X2 ..). In this article, we will learn what is R Squared and Adjusted R Squared, the differences between them and which is better when it comes to model evaluation. What is R Squared? R Squared is used to determine the strength of correlation between the predictors and the target. In simple terms it lets us know how good a regression model is when compared to the average. R Squared is the ratio between the residual sum of squares and the total sum of squares. Where, SSR (Sum of Squares of R