66 Open source projects that are alternatives of or similar to fastshap

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Fit interpretable models. Explain blackbox machine learning.

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

A curated list of awesome machine learning interpretability resources.

Explaining the output of machine learning models with more accurately estimated Shapley values

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Examples of Data Science projects and Artificial Intelligence use cases

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining

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

Debugging, monitoring and visualization for Python Machine Learning and Data Science

A Julia package for interpretable machine learning with stochastic Shapley values

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Predictive AI layer for existing databases.

Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

MindsDB server allows you to consume and expose MindsDB workflows, through http.

GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations

Papers about explainability of GNNs

Implementation of the paper "Shapley Explanation Networks"

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.

Molecular Explanation Generator

An awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences.

Explains machine learning models fast using the Anchor algorithm originally proposed by marcotcr in 2018

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

A repository for explaining feature attributions and feature interactions in deep neural networks.

Variants of Vision Transformer and its downstream tasks

Data generator for Arena - interactive XAI dashboard

Create interactive dashboards for your Convolutional Neural Networks with a single line of code!

Using deep learning to discover interpretable representations for mammogram classification and explanation

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Visualize BERT's self-attention layers on text classification tasks

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Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems

This is a Pytorch implementation of GraphLIME

Library containing attribution and interpretation methods for deep nets.

This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.

Essential NLP & ML, short & fast pure Python code

Paper collection of federated learning. Conferences and Journals Collection for Federated Learning from 2019 to 2021, Accepted Papers, Hot topics and good research groups. Paper summary

⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.

For calculating global feature importance using Shapley values.

Visualization tool for Graph Neural Networks

A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Routines and data structures for using isarn-sketches idiomatically in Apache Spark

Machine Learning in one line of code

The official code of Relevance-CAM

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

Machine Learning Finite State Machine Models from Data with Genetic Algorithms

Multi-Touch Attribution

Concept Bottleneck Models, ICML 2020

Python library for Ceteris Paribus Plots (What-if plots)

Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''

A library for graph deep learning research

Model verification, validation, and error analysis