sklearn.base.RegressorMixin¶
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class
sklearn.base.RegressorMixin[source]¶ Mixin class for all regression estimators in scikit-learn.
Methods
score(X, y[, sample_weight])Return the coefficient of determination R^2 of the prediction.
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__init__(*args, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.
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score(X, y, sample_weight=None)[source]¶ Return 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
- Xarray-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns
- scorefloat
R^2 of self.predict(X) wrt. y.
Notes
The R2 score used when calling
scoreon a regressor usesmultioutput='uniform_average'from version 0.23 to keep consistent with default value ofr2_score. This influences thescoremethod of all the multioutput regressors (except forMultiOutputRegressor).
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