sklearn.base.RegressorMixin¶
- 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 of the prediction.
- score(X, y, sample_weight=None)[source]¶
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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 ofy, 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 with shape
(n_samples, n_samples_fitted), wheren_samples_fittedis 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)w.r.t.y.
Notes
The \(R^2\) 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).
