sklearn.metrics.r2_score(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
[source]
R^2 (coefficient of determination) regression score function.
Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Read more in the User Guide.
Parameters: 


Returns: 

This is not a symmetric function.
Unlike most other scores, R^2 score may be negative (it need not actually be the square of a quantity R).
[1]  Wikipedia entry on the Coefficient of determination 
>>> from sklearn.metrics import r2_score >>> y_true = [3, 0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> r2_score(y_true, y_pred) 0.948... >>> y_true = [[0.5, 1], [1, 1], [7, 6]] >>> y_pred = [[0, 2], [1, 2], [8, 5]] >>> r2_score(y_true, y_pred, multioutput='variance_weighted') ... 0.938... >>> y_true = [1,2,3] >>> y_pred = [1,2,3] >>> r2_score(y_true, y_pred) 1.0 >>> y_true = [1,2,3] >>> y_pred = [2,2,2] >>> r2_score(y_true, y_pred) 0.0 >>> y_true = [1,2,3] >>> y_pred = [3,2,1] >>> r2_score(y_true, y_pred) 3.0
sklearn.metrics.r2_score
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http://scikitlearn.org/stable/modules/generated/sklearn.metrics.r2_score.html