Difference Between R square and Adjusted R square
In many of the supervised machine learning problem statement basically we have two kind of use cases. 1.Regression 2.classification For Regression type use case if we want to check accuracy usually we follow the techniques like R square and adjusted R square. In this article we will be discuss about difference between R square and adjusted R square. 1. R square R square formula is given by, = coefficient of determination = sum of square of residuals or error = total sum of square Here residuals are sum of square of difference between actual point and predict point. and for given data if we have only target variable we will find the best fit line by taking average of all the values. so total sum of square of actual points and average value. So from above formula we will get value between 0 and 1, the more value near to 1 is the best fit line. whether can we get R2 value less than 0? YES, only when your best fit line is worse than average best fit line. If RSS > TSS so ratio beco...