Mixin class for all regression estimators in scikit-learn.
||Returns the coefficient of determination R^2 of the prediction.|
__init__($self, /, *args, **kwargs)
Initialize self. See help(type(self)) for accurate signature.
score(X, y, sample_weight=None)
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The 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.
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