class_docstr: Based class for get_prediction results
      
class_docstr_tok: ["Based","class","for","get_prediction","results"]
docstr: Summary frame
  
  Parameters
  ----------
  alpha : float, optional
      The significance level for the confidence interval.
      ie., The default `alpha` = .05 returns a 95% confidence interval.
  
  Returns
  -------
  pandas DataFrame with columns 'predicted', 'se', 'ci_lower', 'ci_upper'
docstr_tok: ["Summary","frame","Parameters","--","--","--","--","--","alpha",":","float",",","optional","The","significance","level","for","the","confidence","interval",".","ie.",",","The","default","`","alpha","`","=",".05","returns","a","95","%","confidence","interval",".","Returns","--","--","--","-","pandas","DataFrame","with","columns","'predicted","'",",","'se","'",",","'ci_lower","'",",","'ci_upper","'"]
code: def summary_frame(self, alpha=0.05):
      """Summary frame
  
      Parameters
      ----------
      alpha : float, optional
          The significance level for the confidence interval.
          ie., The default `alpha` = .05 returns a 95% confidence interval.
  
      Returns
      -------
      pandas DataFrame with columns 'predicted', 'se', 'ci_lower', 'ci_upper'
      """
      ci = self.conf_int(alpha=alpha)
      to_include = {}
      to_include["predicted"] = self.predicted
      to_include["se"] = self.se
      to_include["ci_lower"] = ci[:, 0]
      to_include["ci_upper"] = ci[:, 1]
  
      self.table = to_include
      # pandas dict does not handle 2d_array
      # data = np.column_stack(list(to_include.values()))
      # names = ....
      res = pd.DataFrame(
          to_include, index=self.row_labels, columns=to_include.keys()
      )
      return res
  
imports: import numpy
  from scipy import stats
  import pandas
label_desc: Use image node_id 7 for calling the PredictionResultsBase obj's underlying member method code with example usage: obj.summary_frame(alpha) and returns: res