def predict_proba(self, X):
        """Probability estimates.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        Returns
        -------
        y_prob : array-like, shape (n_samples, n_classes)
            The predicted probability of the sample for each class in the
            model, where classes are ordered as they are in `self.classes_`.
        """
        y_scores = self.decision_function(X)

        if y_scores.ndim == 1:
            y_scores = logistic(y_scores)
            return np.vstack([1 - y_scores, y_scores]).T
        else:
            return softmax(y_scores)
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neural_network/multilayer_perceptron.py/MLPClassifier.predict_proba/original