0e17f47e9a4920e276bb61b60d6de16264bc6bcf,pythonds/static/single_best.py,SingleBest,predict_proba,#SingleBest#Any#,37

Before Change

    def predict_proba(self, X):
        if "predict_proba" in dir(self.best_clf):
            return self.best_clf.predict_proba(X)
        elif "decision_function" in dir(self.best_clf):
            return softmax(self.best_clf.decision_function(X))
        else:
            raise ValueError("Base classifiers must be able to estimate probabilities.")

After Change

            raise ValueError("Base classifier must support the predict_proba function.")

        predicted_proba = self.best_clf.predict_proba(X)
        return predicted_proba

    def _check_is_fitted(self):
        Verify if the estimator algorithm was fitted.
           Raises an error if it is not fitted.

Instances