sklearn.base.RegressorMixin

class sklearn.base.RegressorMixin [source]

Mixin class for all regression estimators in scikit-learn.

Methods

score(X, y[, sample_weight])

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) [source]

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.

Parameters:

X : array-like, shape = (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.

Returns:

score : float R^2 of self.predict(X) wrt. y.