parameters: (self, name="", flist={}, params={}, kind='s', fold_name='')
docstring: name: Model name
          flist: Feature list
          params: Parameters
          kind: Kind of run() 
          {'s': Stacking only. Svaing a oof prediction({}_all_fold.csv)
                and average of test prediction based on fold-train models({}_test.csv).
           't': Training all data and predict test({}_TestInAllTrainingData.csv).
           'st': Stacking and then training all data and predict test
                 Using save final model with cross-validation
           'cv': Only cross validation without saving the prediction
docstring_summary: name: Model name
          flist: Feature list
          params: Parameters
          kind: Kind of run() 
          {'s': Stacking only. Svaing a oof prediction({}_all_fold.csv)
                and average of test prediction based on fold-train models({}_test.csv).
           't': Training all data and predict test({}_TestInAllTrainingData.csv).
           'st': Stacking and then training all data and predict test
                 Using save final model with cross-validation
           'cv': Only cross validation without saving the prediction
docstring_tokens: name : Model name flist : Feature list params : Parameters kind : Kind of run () { s : Stacking only . Svaing a oof prediction ( {} _all_fold . csv ) and average of test prediction based on fold - train models ( {} _test . csv ) . t : Training all data and predict test ( {} _TestInAllTrainingData . csv ) . st : Stacking and then training all data and predict test Using save final model with cross - validation cv : Only cross validation without saving the prediction
function: def __init__(self, name="", flist={}, params={}, kind='s', fold_name=''):
          '''
          name: Model name
          flist: Feature list
          params: Parameters
          kind: Kind of run() 
          {'s': Stacking only. Svaing a oof prediction({}_all_fold.csv)
                and average of test prediction based on fold-train models({}_test.csv).
           't': Training all data and predict test({}_TestInAllTrainingData.csv).
           'st': Stacking and then training all data and predict test
                 Using save final model with cross-validation
           'cv': Only cross validation without saving the prediction
  
          '''
          self.name = name
          self.flist = flist
          self.params = params
          self.kind = kind
          self.fold_name = fold_name
          assert(self.kind in ['s', 't', 'st', 'cv'])
function_tokens: def __init__ ( self , name = "" , flist = { } , params = { } , kind = 's' , fold_name = '' ) : self . name = name self . flist = flist self . params = params self . kind = kind self . fold_name = fold_name assert ( self . kind in [ 's' , 't' , 'st' , 'cv' ] )