class_docstr: Results class for Gaussian process regression models.
code: def summary(self, yname=None, xname=None, title=None, alpha=0.05):
      df = pd.DataFrame()
  
      typ = (
          ["Mean"] * self.k_exog
          + ["Scale"] * self.k_scale
          + ["Smooth"] * self.k_smooth
      )
      if self._has_noise:
          typ += ["SD"] * self.k_noise
      df["Type"] = typ
  
      df["coef"] = self.params
  
      try:
          df["std err"] = np.sqrt(np.diag(self.cov_params()))
      except Exception:
          df["std err"] = np.nan
  
      from scipy.stats.distributions import norm
  
      df["tvalues"] = df.coef / df["std err"]
      df["P>|t|"] = 2 * norm.sf(np.abs(df.tvalues))
  
      f = norm.ppf(1 - alpha / 2)
      df["[%.3f" % (alpha / 2)] = df.coef - f * df["std err"]
      df["%.3f]" % (1 - alpha / 2)] = df.coef + f * df["std err"]
  
      df.index = self.model.data.param_names
  
      summ = summary2.Summary()
      if title is None:
          title = "Gaussian process regression results"
      summ.add_title(title)
      summ.add_df(df)
  
      return summ
  
imports: import numpy
  import pandas
  import patsy
  import statsmodels.base.model
  from statsmodels.regression.linear_model import OLS
  import collections
  from scipy.optimize import minimize
  from statsmodels.iolib import summary2
  from statsmodels.tools.numdiff import approx_fprime
  import warnings
label_desc: Use image node_id 5 for calling the ProcessMLEResults obj's underlying member method code with example usage: obj.summary(yname, xname, title, alpha) and returns: summ