code: def _roc(y_true, y_score, ax=None):
      
      # check dimensions
  
      fpr, tpr, _ = roc_curve(y_true, y_score)
      roc_auc = auc(fpr, tpr)
  
      ax.plot(fpr, tpr, label=('ROC curve (area = {0:0.2f})'.format(roc_auc)))
      _set_ax_settings(ax)
      return ax
docs: Plot ROC curve for binary classification.
  
      Parameters
      ----------
      y_true : array-like, shape = [n_samples]
          Correct target values (ground truth).
      y_score : array-like, shape = [n_samples]
          Target scores (estimator predictions).
      ax: matplotlib Axes
          Axes object to draw the plot onto, otherwise uses current Axes
  
      Returns
      -------
      ax: matplotlib Axes
          Axes containing the plot