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.

zennit

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

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 models for insurance pricing — EBM tariffs, Actuarial NAM, Pairwise Interaction Networks (PIN), exact Shapley values

endgame-ml

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

shap-relativities

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

trust-free

TRUST - Decision Trees with Sparse Elastic Net leaves, Random Forest accuracy and automated explanations

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 Lattice Modeling Techniques

flash-ansr

Fast Amortized Neural Symbolic Regression with Transformers and SimpliPy

powerlift

Interactive Benchmarking for Machine Learning.

explainax

JAX-based Model Explanation and Interpretation Library

pysr-mcranmer

High-Performance Symbolic Regression in Python and Julia