WOE and IV Binning Workflow

From a raw variable to a kept-or-dropped feature via monotonic WOE bins and an IV gate

WOE and IV Binning Workflow From a raw variable to a kept-or-dropped feature via monotonic WOE bins and an IV gate 01 / Source Data 02 / Binning Engine 03 / WOE + IV Scoring 04 / Feature Selection EX / Re-bin + Special Values Monotonic bin WOE + IV Select Monotonic binning Corrective work Raw Variable · predictor + target · Source Data › Monotonic bin Raw Variable predictor + target Fine Bins · quantile splits · Binning Engine › Monotonic binning › Monotonic bin Fine Bins quantile splits Monotonic? · WOE ordered · Binning Engine › Monotonic binning › Monotonic bin · gate Monotonic? WOE ordered gate Compute WOE · ln(good% / bad%) · WOE + IV Scoring › WOE + IV Compute WOE ln(good% / bad%) Compute IV · sum over bins · WOE + IV Scoring › WOE + IV Compute IV sum over bins Keep + Rank · IV >= 0.02 · Feature Selection › Select · scorecard Keep + Rank IV >= 0.02 scorecard Drop Feature · weak IV · Re-bin + Special Values › Corrective work › Select Drop Feature weak IV Merge Bins · combine adjacent · Re-bin + Special Values › Corrective work › Monotonic bin Merge Bins combine adjacent Own Bin · missing / rare · Re-bin + Special Values › Corrective work › Monotonic bin Own Bin missing / rare not monotonic IV < 0.02 missing / rare Legend User UI Agent logic Policy Tool action Context / trace

One Readable Path

  • • Every variable becomes a small bins-to-WOE table
  • • Monotonic WOE keeps each risk relationship defensible
  • • IV gives a single comparable strength score per feature

Correct Before Scoring

  • • A non-monotonic trend triggers a merge and re-check
  • • Missing and rare values earn their own bin, not imputation
  • • An implausibly high IV is investigated as possible leakage

Selection Evidence

  • • Cut points and WOE tables are reproducible for validators
  • • The IV band decides keep versus drop
  • • Bin distributions can be monitored for population drift

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