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Equal Frequency

Context

Algorithm Details & Recommendations

  • This Equal Frequency algorithm defines thresholds so that each interval contains the same number of observations.
  • This approach typically produces a uniform distribution.
  • As a result, the shape of the original density function is no longer apparent upon discretization.
  • This also leads to an artificial increase in the entropy of the system, directly affecting the complexity of machine-learned models.
  • However, this type of discretization can be useful — once a structure is learned — for further increasing the precision of the representation of continuous values.

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