csinva/imodels

Health v2 · maintenance only

Adoption not part of the score

1617 stars · 141 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python package providing scikit-learn-compatible implementations of interpretable machine-learning models such as rule lists, RuleFit, and optimal classification trees. It lets users fit transparent predictive models with the standard fit/predict interface, often replacing black-box models without sacrificing accuracy.

Use cases

• fit interpretable machine learning models in python
• replace random forest with a transparent rule list
• train sklearn-compatible rule-based classifiers
• build explainable decision trees with hierarchical shrinkage
• get concise predictive models for clinical or tabular data
• learn rulefit or bayesian rule lists in python

When to choose

• you need models whose predictions humans can inspect and explain
• you want drop-in scikit-learn-compatible interpretable classifiers and regressors
• regulatory or trust requirements demand transparent modeling

When to avoid

• you need maximum predictive accuracy regardless of interpretability
• you work outside Python or outside the scikit-learn ecosystem
• you need deep learning or large-scale black-box modeling

Facets

library · maturity active

machine-learning data-science machine-learning data-science artificial-intelligence python interpretable-ml explainable-ai rule-learning scikit-learn-compatible rulefit bayesian-rule-list optimal-trees statistics

Member repositories

For agents

markdown · JSON · MCP: product_card(name="csinva/imodels")