Interpretable Ml Python Packages

interpret-core

Fit interpretable models. Explain blackbox machine learning.

captum

Model interpretability and understanding for PyTorch

interpret

Fit interpretable models. Explain blackbox machine learning.

aplr

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

flash-ansr

Fast Amortized Neural Symbolic Regression with Transformers and SimpliPy

zennit

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

dna-decode

Deterministic, interpretable genome→phenotype decoder: calls antibiotic/antiviral/antifungal resistance from DNA + names the exact genes/mutations driving each call. Spans bacteria, M. tuberculosis, fungi, HIV-1 & SARS-CoV-2 — independently validated on FREE measured wet-lab labels (EBI AMR Portal, Stanford HIVDB). Not a clinical tool.

adcd

Discover physical correction laws from anomalous data in JAX.

trust-free

An interpretable regression model in Python with Random-Forest-level accuracy

pyss3

A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI :octocat:

insurance-gam

Interpretable GAM toolkit for insurance pricing: EBM, Neural Additive Models, Pairwise Interaction Networks, and debiased post-selection GLM inference for UK actuarial modelling.

shap-relativities

SHAP-based rating relativities from GBM models for insurance pricing — extract GLM-style factors from CatBoost, Polars

endgame-ml

A unified framework for tabular, time-series, and multimodal machine learning

powerlift

Fit interpretable models. Explain blackbox machine learning.

mlm-bias

Measuring Biases in Masked Language Models for PyTorch Transformers. Support for multiple social biases and evaluation measures.

pytorch-lattice

A PyTorch implementation of constrained optimization and modeling techniques

explainax

JAX-based Model Explanation and Interpretation Library

pysr-mcranmer

High-Performance Symbolic Regression in Python and Julia