Method decision_function

sklearn/utils/_mocking.py:284–309

Confidence score. Parameters ---------- X : array-like of shape (n_samples, n_features) The input data. Returns ------- decision : ndarray of shape (n_samples,) if n_classes == 2\ else (n_samples, n_classes) Co

(self, X)

        return proba

    def decision_function(self, X):
        """Confidence score.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The input data.

        Returns
        -------
        decision : ndarray of shape (n_samples,) if n_classes == 2\
                else (n_samples, n_classes)
            Confidence score.
        """
        if (
            self.methods_to_check == "all"
            or "decision_function" in self.methods_to_check
        ):
            X, y = self._check_X_y(X)
        rng = check_random_state(self.random_state)
        if len(self.classes_) == 2:
            # for binary classifier, the confidence score is related to
            # classes_[1] and therefore should be null.
            return rng.randn(_num_samples(X))
        else:
            return rng.randn(_num_samples(X), len(self.classes_))

    def score(self, X=None, Y=None):
        """Fake score.