class_docstr: Train/Validation set splitter for sklearn's GridSearchCV etc.
  
  Provides train/validation set indices to split a dataset
  into train/validation sets using random indices.
  
  Parameters
  ----------
  valid_size : float (default: 0.5)
      Proportion of examples that being assigned as
      validation examples. 1-`valid_size` will then automatically
      be assigned as training set examples.
  random_seed : int (default: None)
      The random seed for splitting the data
      into training and validation set partitions.
  stratify : bool (default: False)
      True or False, whether to perform a stratified
      split or not
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/evaluate/RandomHoldoutSplit/
class_docstr_tok: ["Train\/Validation","set","splitter","for","sklearn","'s","GridSearchCV","etc",".","Provides","train\/validation","set","indices","to","split","a","dataset","into","train\/validation","sets","using","random","indices",".","Parameters","--","--","--","--","--","valid_size",":","float","(","default",":","0.5",")","Proportion","of","examples","that","being","assigned","as","validation","examples",".","1-","`","valid_size","`","will","then","automatically","be","assigned","as","training","set","examples",".","random_seed",":","int","(","default",":","None",")","The","random","seed","for","splitting","the","data","into","training","and","validation","set","partitions",".","stratify",":","bool","(","default",":","False",")","True","or","False",",","whether","to","perform","a","stratified","split","or","not","Examples","--","--","--","--","--","-","For","usage","examples",",","please","see","https",":","\/\/rasbt.github.io\/mlxtend\/user_guide\/evaluate\/RandomHoldoutSplit\/"]
code: def __init__(self, valid_size=0.5, random_seed=None, stratify=False):
      self.valid_size = valid_size
      self.random_seed = random_seed
      self.stratify = stratify
  
file_path: mlxtend/mlxtend/evaluate/holdout.py
imports: import numpy
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 1 to create a new RandomHoldoutSplit object from inherited base classes: object with example: obj = RandomHoldoutSplit(valid_size, random_seed, stratify)