How to use the scikit-learn.sklearn.base.RegressorMixin function in scikit-learn

Returns
        -------
        T : array-like, shape (n_samples, n_classes)
            Returns the log-probability of the sample for each class in the
            model, where classes are ordered as they are in
            `self.classes_`.
        """
        self._check_proba()
        return self._predict_log_proba

    def _predict_log_proba(self, X):
        return np.log(self.predict_proba(X))


class BaseSGDRegressor(BaseSGD, RegressorMixin):

    loss_functions = {
        "squared_loss": (SquaredLoss, ),
        "huber": (Huber, DEFAULT_EPSILON),
        "epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
        "squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
                                        DEFAULT_EPSILON),
    }

    @abstractmethod
    def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001,
                 l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True,
                 verbose=0, epsilon=DEFAULT_EPSILON, random_state=None,
                 learning_rate="invscaling", eta0=0.01, power_t=0.25,
                 warm_start=False, average=False, n_jobs=1):
        super(BaseSGDRegressor, self).__init__(loss=loss, penalty=penalty,