Comment clf.score (X_test, Y_test) fonctionne pour la régression linéaire?

Question

Y_predicted =

[ 1.22770607 -0.04569864 2.23941551 1.35143415 1.28991445 0.01398049 
    1.05511961 1.84491163 1.36748873 1.06369944 1.7139329 -0.09376361 
-0.16512009 -0.0847085 -0.03141076 1.40425663 2.01371189 1.03977449 
    1.27667011 1.98481437 0.01295936 1.60489574 0.0823478 1.92856356 
    1.8584911 1.8970799 1.79401204 2.05206755 0.018738 0.00754765 
-0.14906387 -0.0721375 1.18370287 -0.00857916 -0.03033274 1.69166794 
    1.29215806 -0.08232208 -0.09134106 -0.1708265 1.76007136 1.38080694 
    1.31560775 -0.06333357 -0.11464793 0.94925056 1.47575594 1.71846632 
    1.19986579 2.15035351 1.19564044 2.04054474 1.38920784 -0.09675278 
    2.00947585 1.00163388 -0.1021645 -0.05493268 0.12571214 0.97425353] 

Y_test =

[2 0 0 0 1 0 1 2 0 1 2 0 2 2 1 1 2 1 0 1 2 0 0 1 1 0 2 0 0 1 1 2 1 2 2 1 0 
0 2 2 0 0 0 1 2 0 2 2 0 1 1 2 1 2 0 2 1 2 1 1 1 0 1 1 0 1 2 2 0 1 2 2 0 2 
0 1 2 2 1 2 1 1 2 2 0 1 2 0 1 2] 

Mais le score est 94,2%

Comment calcule-t-classificateur ce score?

Answer 1

De l'documentation

Returns the coefficient of determination R^2 of the prediction. 
The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares 
((y_true - y_pred) ** 2).sum() and v is the residual sum of squares 
((y_true - y_true.mean()) ** 2).sum().