imodels
A Python package for concise, transparent, and accurate predictive modeling with sklearn-compatible interpretable models.
What is imodels?
imodels is a Python package that provides a unified scikit-learn-compatible interface for fitting and using state-of-the-art interpretable machine learning models. It addresses the complexity of modern ML by offering simpler, transparent models like rule sets, rule lists, rule trees, and algebraic models that can often replace black-box alternatives without sacrificing predictive accuracy. The package includes novel algorithms such as FIGS (Fast Interpretable Greedy-Tree Sums) and Hierarchical Shrinkage for post-hoc tree regularization.
Data scientists and machine learning practitioners working on tabular data who need interpretable models for binary classification or regression tasks, particularly in domains like healthcare, finance, or any field requiring model transparency and compliance. Researchers developing or benchmarking interpretable ML methods will also find it useful.
Developers choose imodels because it offers a comprehensive, sklearn-compatible library of interpretable models in one package, simplifying integration into existing workflows. Its unique selling point is providing access to both established and novel algorithms (like FIGS and Hierarchical Shrinkage) that maintain high accuracy while being inherently explainable, often outperforming black-box models in interpretability and efficiency.
Overview
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
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