Interpretable Ai Python Packages

interpret-core

Fit interpretable models. Explain blackbox machine learning.

captum

Model interpretability and understanding for PyTorch

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.

interpret

Fit interpretable models. Explain blackbox machine learning.

grad-cam

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

zennit

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

prosemble

A python package for prototype-based machine learning models

desdeo-brb

A trainable Belief Rule-Based (BRB) inference system with an sklearn-compatible API and optional JAX backend for differentiable training.

te2rules

Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.

shapley-lz

IN PROGRESS - after the paper "Shapley-Lorenz decompositions in eXplainable Artificial Intelligence" by Giudici and Raffinetti - 2020

explainx

Explain & debug any blackbox machine learning model with a single line of code.

pytorch-sidu

SIDU: SImilarity Difference and Uniqueness method for explainable AI

torch-explain

PyTorch Explain: Interpretable Deep Learning in Python.

prototorch

ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in prototype-based machine learning algorithms.

xpotato

XAI based human-in-the-loop framework for automatic rule-learning.

powerlift

Fit interpretable models. Explain blackbox machine learning.

tslens

SOTA Time Series Interpretability Library for PyTorch, from Supervised to Foundation Models

lorenz-zonoid

IN PROGRESS - after the paper "Shapley-Lorenz decompositions in eXplainable Artificial Intelligence" by Giudici and Raffinetti - 2020

deep-xf

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

cromp

The official implementation of CROMP (Constrained Regression with Ordered and Margin-sensitive Parameters) along with experimental test pipeline

bioexp

Explainability of Deep Learning Models

mlm-bias

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

nafes

A python project for prototype-based feature selection

pytorch-lattice

A PyTorch implementation of constrained optimization and modeling techniques