Search 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.

insurance-gam

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

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:

shap-relativities

Extract multiplicative rating relativities from GBM models using SHAP values. Built for insurance pricing.

trust-free

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

endgame-ml

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

powerlift

Fit interpretable models. Explain blackbox machine learning.

pytorch-lattice

A PyTorch implementation of constrained optimization and modeling techniques

mlm-bias

Bias Evaluation Methods for Masked Language Models implemented in PyTorch

flash-ansr

Fast Amortized Neural Symbolic Regression with Transformers and SimpliPy

explainax

JAX-based Model Explanation and Interpretation Library

pysr-mcranmer

Simple and efficient symbolic regression