aclai-lab/SoleModels.jl

Symbolic modeling in Julia!

37
/ 100
Emerging

This tool helps data scientists, machine learning engineers, and researchers simplify complex decision-making processes. It takes in structured data (like customer demographics or medical readings) and produces easy-to-understand symbolic models, such as decision trees or rules, which reveal the underlying logic behind predictions. The output helps users interpret how decisions are made, making models more transparent and trustworthy.

Use this if you need to understand the 'why' behind your model's predictions and extract clear, human-readable rules from your data.

Not ideal if your primary goal is only black-box predictive accuracy without needing model interpretability or rule extraction.

interpretable-AI decision-making-logic machine-learning-explainability rule-based-systems model-transparency
No Package No Dependents
Maintenance 10 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 6 / 25

How are scores calculated?

Stars

12

Forks

1

Language

Julia

License

MIT

Last pushed

Mar 09, 2026

Commits (30d)

0

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