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Simplicity Over Black Boxes - Towards Data Science
Human Knowledge Models (HKM) is an innovative method that transforms complex machine learning models into simple, interpretable Boolean rules (AND, OR, NOT) while maintaining comparable performance. T
his approach bridges the gap between AI capability and human understanding, making it particularly valuable in fields like healthcare where interpretability is crucial.

Reasons to Read -- Learn:

  • how to simplify complex ML models into human-readable rules that achieve comparable performance while using just 1-4 Boolean expressions, as demonstrated in the churn prediction example where HKM matched XGBoost's performance with significantly fewer rules (1 vs 1647)
  • an alternative approach to ML deployment that doesn't require complex infrastructure, as HKM rules can be implemented directly in front-end code, making it particularly valuable for small-scale applications or when ML infrastructure costs are a concern
  • how to make ML models more trustworthy for domain experts, as demonstrated in the medical example where doctors can easily understand and adjust predictions based on clear rules like 'if blood pressure > 120 and temperature > 38°C, then pneumonia risk is high'
  • 7 min readauthor: Vladimir Zhyvov
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