What is the use of the R-squared value?
The r-squared value compares the variation of a fitted curve to a set of data points with the variation of those points wrt the line that passes through the average value. It can be understood with the help of the formula R2 = [Var(mean) - Var(model)] / Var(mean) It is obvious that the model is likely to fit better than the average line. So, the variation for the model is likely to be less than the variation for the line. Thus, if the r-square has a value of 0.92, it suggests that the model fits the data points better than the line as there is 92% less variation. It also shows that there is a strong correlation between the feature and target value. However, if the r-squared value is less, it suggests that the correlation is weak and the two variables are quite independent of each other.
