def predict(self, x, nc, significance=None):
		prediction = self._underlying_predict(x)
		if significance:
			return self.inverse_err_func(prediction, nc, significance)
		else:
			significance = np.arange(0.01, 1.0, 0.01)
			return np.dstack([self.inverse_err_func(prediction, nc, s)
			                  for s in significance])
Constructs prediction intervals for a set of test examples.
dataset/ETHPy150Open donlnz/nonconformist/nonconformist/nc.py/RegressorNc.predict