class_docstr: BaseCV class. It computes the cross validation error of a given model.
  All the cross validation classes can be derived by this one
  (e.g. GamCV, LassoCV,...)
class_docstr_tok: ["BaseCV","class",".","It","computes","the","cross","validation","error","of","a","given","model",".","All","the","cross","validation","classes","can","be","derived","by","this","one","(","e.g",".","GamCV",",","LassoCV",",","...",")"]
code: def fit(self, **kwargs):
      # kwargs are the input values for the fit method of the
      # cross-validated object
  
      cv_err = []
  
      for train_index, test_index in self.train_test_cv_indices:
          cv_err.append(self._error(train_index, test_index, **kwargs))
  
      return np.array(cv_err)
  
imports: from abc import ABCMeta, abstractmethod
  from statsmodels.compat.python import with_metaclass
  import itertools
  import numpy
  from statsmodels.gam.smooth_basis import GenericSmoothers, UnivariateGenericSmoother
label_desc: Use image node_id 2 for calling the BaseCV obj's underlying member method code with example usage: obj.fit() and returns: np