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.

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

te2rules

Python Library to explain tree ensembles using rules

torch-explain

PyTorch Explain: Interpretable Deep Learning in Python.

explainx

Explain and debug any black-box Machine Learning model.

pytorch-sidu

SIDU: SImilarity Difference and Uniqueness method for explainable AI

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.

shapley-lz

Computes the Shapley Lorenz Zonoid share of a set of covariates

powerlift

Fit interpretable models. Explain blackbox machine learning.

desdeo-brb

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

lorenz-zonoid

Computes the Shapley Lorenz Zonoid share of covariates

deep-xf

DEEPXF - An open-source, low-code explainable forecasting and nowcasting library with state-of-the-art deep neural networks and Dynamic Factor Model. Now available with additional addons like Denoising TS signals with ensembling of filters, TS signal similarity test with Siamese Neural Networks

pytorch-lattice

A PyTorch implementation of constrained optimization and modeling techniques

mlm-bias

Bias Evaluation Methods for Masked Language Models implemented in PyTorch

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

nafes

A python project for prototype-based feature selection

explainax

JAX-based Model Explanation and Interpretation Library