class_docstr: Higgs dataset.
  
  The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22)
  are kinematic properties measured by the particle detectors in the accelerator. The last seven
  features are functions of the first 21 features; these are high-level features derived by
  physicists to help discriminate between the two classes.
  
  References
  ----------
  [^1]: [UCI page](https://archive.ics.uci.edu/ml/datasets/HIGGS)
code: def _iter(self):
      features = [
          "lepton pT",
          "lepton eta",
          "lepton phi",
          "missing energy magnitude",
          "missing energy phi",
          "jet 1 pt",
          "jet 1 eta",
          "jet 1 phi",
          "jet 1 b-tag",
          "jet 2 pt",
          "jet 2 eta",
          "jet 2 phi",
          "jet 2 b-tag",
          "jet 3 pt",
          "jet 3 eta",
          "jet 3 phi",
          "jet 3 b-tag",
          "jet 4 pt",
          "jet 4 eta",
          "jet 4 phi",
          "jet 4 b-tag",
          "m_jj",
          "m_jjj",
          "m_lv",
          "m_jlv",
          "m_bb",
          "m_wbb",
          "m_wwbb",
      ]
  
      return stream.iter_csv(
          self.path,
          fieldnames=["is_signal", *features],
          target="is_signal",
          converters={
              "is_signal": lambda x: x.startswith("1"),
              **{f: float for f in features},
          },
      )
  
imports: from __future__ import annotations
  from river import stream
  from .None import base
label_desc: Use image node_id 2 for calling the Higgs obj's underlying member method code with example usage: obj._iter() and returns: stream