class_docstr: SMS Spam Collection dataset.
  
  The data contains 5,574 items and 1 feature (i.e. SMS body). Spam messages represent
  13.4% of the dataset. The goal is to predict whether an SMS is a spam or not.
  
  References
  ----------
  [^1]: [Almeida, T.A., Hidalgo, J.M.G. and Yamakami, A., 2011, September. Contributions to the study of SMS spam filtering: new collection and results. In Proceedings of the 11th ACM symposium on Document engineering (pp. 259-262).](http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/doceng11.pdf)
class_docstr_tok: ["SMS","Spam","Collection","dataset",".","The","data","contains","5,574","items","and","1","feature","(","i.e",".","SMS","body",")",".","Spam","messages","represent","13.4","%","of","the","dataset",".","The","goal","is","to","predict","whether","an","SMS","is","a","spam","or","not",".","References","--","--","--","--","--","[","^1","]",":","[","Almeida",",","T.A.",",","Hidalgo",",","J.M.G",".","and","Yamakami",",","A.",",","2011",",","September",".","Contributions","to","the","study","of","SMS","spam","filtering",":","new","collection","and","results",".","In","Proceedings","of","the","11th","ACM","symposium","on","Document","engineering","(","pp",".","259-262",")",".","]","(","http",":","\/\/www.dt.fee.unicamp.br\/~tiago\/smsspamcollection\/doceng11.pdf",")"]
code: def __init__(self):
      super().__init__(
          n_samples=5_574,
          n_features=1,
          task=base.BINARY_CLF,
          url="https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip",
          size=477_907,
          filename="SMSSpamCollection",
      )
  
imports: from __future__ import annotations
  from .None import base
label_desc: Use image node_id 1 to create a new SMSSpam object from inherited base classes: base with example: obj = SMSSpam()