RegressorMixin (original) (raw)

class sklearn.base.RegressorMixin[source]#

Mixin class for all regression estimators in scikit-learn.

This mixin defines the following functionality:

• set estimator type to "regressor" through the estimator_type tag;
• score method that default to r2_score.
• enforce that fit requires y to be passed through the requires_y tag, which is done by setting the regressor type tag.

Read more in the User Guide.

Examples

import numpy as np from sklearn.base import BaseEstimator, RegressorMixin

Mixin classes should always be on the left-hand side for a correct MRO

class MyEstimator(RegressorMixin, BaseEstimator): ... def init(self, *, param=1): ... self.param = param ... def fit(self, X, y=None): ... self.is_fitted_ = True ... return self ... def predict(self, X): ... return np.full(shape=X.shape[0], fill_value=self.param) estimator = MyEstimator(param=0) X = np.array([[1, 2], [2, 3], [3, 4]]) y = np.array([-1, 0, 1]) estimator.fit(X, y).predict(X) array([0, 0, 0]) estimator.score(X, y) 0.0

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 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 with shape(n_samples, n_samples_fitted), where n_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 score on a regressor usesmultioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except forMultiOutputRegressor).

Gallery examples#