What is R Squared for Linear Regression

For Linear Regression, R-squared is a statistical term which indicates how close the data are to the fitted regression line.

R-Squared is also known as coefficient of determination.

R-squared = Explained variation in data / Total variation in data

R-squared = 1 – (RSS/TSS)

RSS = Sum of squares of difference between predicted value and actual value

TSS = Sum of squares of difference between mean value and actual value

How to calculate R-Squared using Sklearn for Linear Regression.

from sklearn.metrics import r2_score

rsquared = r2_score(y_true,y_pred)

Below picture depicts how all the data point may not fall on the fitted regression line.

Note that the value of R-squared does not indicate the performance of the Linear Regression model, hence you should analyze residuals by calculating Root Mean Squared Error or RMSE as well. 

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