483 Open source projects that are alternatives of or similar to shapr

Fast approximate Shapley values in R

Fast Unit Root Tests and OLS regression in C++ with wrappers for R and Python

Text Processing for Small or Big Data Files in R

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

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

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).

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

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

Fit interpretable models. Explain blackbox machine learning.

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

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

Examples of Data Science projects and Artificial Intelligence use cases

Predictive AI layer for existing databases.

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

Rcpp integration for the Ensmallen templated C++ mathematical optimization library

An R package for space-time anomaly detection using scan statistics.

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

RcppMeCab: Rcpp Interface of CJK Morpheme Analyzer MeCab

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

Library containing attribution and interpretation methods for deep nets.

XPtr Add-Ons for 'Rcpp'

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

This is a Pytorch implementation of GraphLIME

A Julia package for interpretable machine learning with stochastic Shapley values

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

For calculating global feature importance using Shapley values.

The official code of Relevance-CAM

No description or website provided.

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

R and ArrayFire library via Rcpp

⛔ ARCHIVED ⛔ Set Up 'Travis' for Testing and Deployment

Inline C, C++ or Fortran functions in R

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

📸 Generate Webpage Screenshots Using the Statically API

Faster hash maps in R

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

Molecular Explanation Generator

Elastic, reproducible, and reusable genomic data science tools from R backed by cloud resources

Create Voronoi and Sunburst Treemaps from Hierarchical data

Rcpp Bindings for the 'fastfloat' Header-Only Library

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.

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Rcpp interface to sparsepp - efficient hash map for C++

User-Friendly R Package for Supervised Machine Learning Pipelines

Source for the Unofficial Rcpp API Documentation

An implementation of the Artificial Bee Colony (ABC) Algorithm

R package providing access and examples to TRNG C++ library

Multi-Touch Attribution

Implementation of the paper "Shapley Explanation Networks"

[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.

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

RcppProgress R package: An interruptible progress bar with OpenMP support for c++ in R packages

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

An R wrapper for CanIStream.It API

Papers about explainability of GNNs

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

Build sf objects from R and Rcpp

R Package: Regularized Principal Component Analysis for Spatial Data

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

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