RULEX AI

At the core of Rulex is a family of proprietary machine learning algorithms which are different from conventional machine learning algorithms, like Decision Trees, Neural Nets, and others.

 

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Decision Trees and Random Forests produce predictive models that are readable, but not understandable.

Rulex allows overlapping rules for simpler, more understandable predictions than Decision Trees.

RULEX FOR MANUAL AND AUTOMATED MACHINE LEARNING

NEURAL NETWORKS,
SVMS, ETC.

  • Users are data scientists
  • Requires math and programming
  • Manual data exploration
  • Manual, experimental modeling
  • Math function-based models
  • Black box predictive decisions
  • Prediction on central servers
  • New predictions require re-coding

RULEX LOGIC LEARNING

  • Users are business/process experts
  • No new skills required
  • Automatic data discovery
  • Automatic model building
  • If-then rules-based models
  • Self-explanatory predictions
  • Prediction on edge devices
  • Update predictions w/o re-coding
Neural Nets, as well as Support Vector Machines and most other popular math-based algorithms produce math functions that are neither readable nor understandable.
Neural Nets, as well as Support Vector Machines and most other popular math-based algorithms produce math functions that are neither readable nor understandable.