Jan-08-2020, 04:23 PM

Hi guys,

As far as I know you can calculate the accuracy of a regression model with sklearn LinearRegression

The code would be something like this :

model = LinearRegression()

accuracy = model.score(X_test, y_test)

and print(accuracy) would give you a value for how good the model performs in predicting y.

Then there also exists r2_score from sklearn metrics.

Is there a difference between those two values ? If so, what is the difference ?

thanks in advance!

As far as I know you can calculate the accuracy of a regression model with sklearn LinearRegression

The code would be something like this :

model = LinearRegression()

accuracy = model.score(X_test, y_test)

and print(accuracy) would give you a value for how good the model performs in predicting y.

Then there also exists r2_score from sklearn metrics.

Is there a difference between those two values ? If so, what is the difference ?

thanks in advance!