Interpretable Ai 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.

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

torch-explain

PyTorch Explain: Interpretable Deep Learning in Python.

pytorch-sidu

SIDU: SImilarity Difference and Uniqueness method for explainable AI

te2rules

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

prototorch

Highly extensible, GPU-supported Learning Vector Quantization (LVQ) toolbox built using PyTorch and its nn API.

explainx

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu

shapley-lz

Computes the Shapley Lorenz Zonoid share of a set of covariates

desdeo-brb

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

xpotato

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

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.

lorenz-zonoid

Computes the Shapley Lorenz Zonoid share of covariates

mlm-bias

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

cromp

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

pytorch-lattice

A PyTorch Implementation Of Lattice Modeling Techniques

powerlift

Interactive Benchmarking for Machine Learning.

bioexp

Explainability of Deep Learning Models

nafes

A python project for prototype-based feature selection

explainax

JAX-based Model Explanation and Interpretation Library