code: def predict(self, X):
          
          X = _check_2d_inp(X)
          return np.argmax(self.decision_function(X), axis=1)
docs: Predict the less costly class for a given observation
          
          Parameters
          ----------
          X : array (n_samples, n_features)
              Data for which to predict minimum cost label.
          
          Returns
          -------
          y_hat : array (n_samples,)
              Label with expected minimum cost for each observation.