'Estimate probabilistic estimates of each data slice with all possible estimators.
Parameters
X : array, shape (n_samples, nd_features, n_slices)
The training input samples. For each data slice, a fitted estimator
predicts a slice of the data. The feature dimension can be
multidimensional e.g.
``X.shape = (n_samples, n_features_1, n_features_2, n_estimators)``.
Returns
y_pred : array, shape (n_samples, n_estimators, n_slices, n_classes)
The predicted values for each estimator.
Notes
This requires base_estimator to have a `predict_proba` method.'

def predict_proba(self, X):

    return self._transform(X, 'predict_proba')
