code: def _get_quantile_ratio(self, X, y):
          
          y_pred = self.predict(X)
          return (y_pred > y).mean()
docs: find the expirical quantile of the model
  
          Parameters
          ----------
          X : array-like, shape (n_samples, m_features)
              Training vectors, where n_samples is the number of samples
              and m_features is the number of features.
          y : array-like, shape (n_samples,)
              Target values (integers in classification, real numbers in
              regression)
              For classification, labels must correspond to classes.
  
          Returns
          -------
          ratio : float on [0, 1]