class_docstr: Based class for get_prediction results
      
class_docstr_tok: ["Based","class","for","get_prediction","results"]
docstr: Confidence interval for the predicted value.
  
  Parameters
  ----------
  alpha : float, optional
      The significance level for the confidence interval.
      ie., The default `alpha` = .05 returns a 95% confidence interval.
  
  kwds : extra keyword arguments
      Ignored in base class, only for compatibility, consistent signature
      with subclasses
  
  Returns
  -------
  ci : ndarray, (k_constraints, 2)
      The array has the lower and the upper limit of the confidence
      interval in the columns.
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code: def conf_int(self, *, alpha=0.05, **kwds):
      """Confidence interval for the predicted value.
  
      Parameters
      ----------
      alpha : float, optional
          The significance level for the confidence interval.
          ie., The default `alpha` = .05 returns a 95% confidence interval.
  
      kwds : extra keyword arguments
          Ignored in base class, only for compatibility, consistent signature
          with subclasses
  
      Returns
      -------
      ci : ndarray, (k_constraints, 2)
          The array has the lower and the upper limit of the confidence
          interval in the columns.
      """
  
      ci = self._conf_int_generic(
          self.predicted, self.se, alpha, dist_args=self.dist_args
      )
      return ci
  
imports: import numpy
  from scipy import stats
  import pandas
label_desc: Use image node_id 6 for calling the PredictionResultsBase obj's underlying member method code with example usage: obj.conf_int() and returns: ci