Calculating error from accuracy score in Sklearn I have one question thats maybe simple, but my brain overfited :)

I wrote a code for simple linear regression in Python and Scikit-Learn, and I calculated score of my model.

        accuracy_test = model.score(x_test, y_test)


I got the score of 0.97. Does that mean that my error is 0.03 (3%)?
For example, Is it correct to say, that If i predict some value with model.predict function, and I get result of 100 with that, that I can say that my result will be in range from 97 to 103 because of my 3% error (score of 0.97)?

Can you say this for accuracy_score also?
From sklearn's documentation for the score function

Returns the coefficient of determination R^2 of the prediction.


R^2 is a measure of how well the variability of the data is explained by the model. So, at 0.97, the model is able to explain that really well. And yes, it is a measure of accuracy for regression models. However, you cannot construct confidence intervals with that estimate. 

accuracy_score is a measure of accuracy for classification models. Again, you cannot construct confidence intervals from the accuracy metric.