Python - Coefficient of Determination-R2 score

Last Updated : 20 Aug, 2025

The Coefficient of determination, also called R² score, is used to evaluate the performance of a linear regression model. It is the amount of the variation in the output dependent attribute that is predictable from the input independent variable(s). It is used to check how well-observed results are reproduced by the model, depending on the ratio of the total deviation of results described by the model.

Mathematical Formula:

R^2= 1- \frac{SS_{res}}{SS_{tot}}

Where,

  • SS_{res} is the sum of squares of the residual errors
  • SS_{tot} is the total sum of the errors

Interpretation of R2 score

Assume R2 = 0.68. It can be inferred that 68% of the changeability of the dependent output attribute can be explained by the model, while the remaining 32 % of the variability is still unaccounted for. R2 indicates the proportion of data points that lie within the line created by the regression equation. A higher value of R2 is desirable as it indicates better results.

Examples

Case 1 (Model gives accurate results):

R^2 = 1 - \frac{0}{200} = 1


Case 2 (Model gives same results always):

R^2 = 1 - \frac{200}{200} = 0

Case 3 (Model gives ambiguous results):

R^2 = 1 - \frac{600}{200} = -2

We can import r2_score from sklearn.metrics in Python to compute R2 score.

Python Implementation

Step 1: Import r2_score from sklearn.metrics

Python 
from sklearn.metrics import r2_score


Step 2: Calculate R2 score for all the above cases.

Python 
### Assume y is the actual value and f is the predicted values
y =[10, 20, 30]
f =[10, 20, 30]
r2 = r2_score(y, f)
print('r2 score for perfect model is', r2)

Output:

r2 score for perfect model is 1.0


Python 
### Assume y is the actual value and f is the predicted values
y =[10, 20, 30]
f =[20, 20, 20]
r2 = r2_score(y, f)
print('r2 score for a model which predicts mean value always is', r2)

Output:

r2 score for a model which predicts mean value always is 0.0


Code 3:

Python 
### Assume y is the actual value and f is the predicted values
y = [10, 20, 30]
f = [30, 10, 20]
r2 = r2_score(y, f)
print('r2 score for a worse model is', r2)

Output:

r2 score for a worse model is -2.0

Conclusion

  • The best possible score is 1 which is obtained when the predicted values are the same as the actual values.
  • R2 score of baseline model is 0.
  • During the worse cases, R2 score can even be negative.
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