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133 search results found

Deep Learning Drizzle ⭐ 10,767

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Pytorch Grad Cam ⭐ 8,723

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

Interpret ⭐ 5,992

Fit interpretable models. Explain blackbox machine learning.

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

Tensorwatch ⭐ 3,333

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

Giskard ⭐ 2,509

🐢 The testing framework for ML models, from tabular to LLMs

High-Performance Symbolic Regression in Python and Julia

Aix360 ⭐ 1,533

Interpretability and explainability of data and machine learning models

Generate Diverse Counterfactual Explanations for any machine learning model.

Dalex ⭐ 1,242

moDel Agnostic Language for Exploration and eXplanation

Imodels ⭐ 1,229

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Responsible Ai Toolbox ⭐ 1,187

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Transformers Interpret ⭐ 1,090

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

XAI - An eXplainability toolbox for machine learning

Awesome Interpretable Machine Learning ⭐ 856

Lofo Importance ⭐ 785

Leave One Feature Out Importance

Omnixai ⭐ 674

OmniXAI: A Library for eXplainable AI

Awesome Open Data Centric Ai ⭐ 630

Curated list of open source tooling for data-centric AI on unstructured data.

Ml_privacy_meter ⭐ 501

Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.

Awesome Graph Explainability Papers ⭐ 486

Papers about explainability of GNNs

Fasttreeshap ⭐ 485

Fast SHAP value computation for interpreting tree-based models

Symbolicregression.jl ⭐ 484

Distributed High-Performance Symbolic Regression in Julia

Human-explainable AI.

Quantus ⭐ 460

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

Machine_learning_tutorials ⭐ 421

Code, exercises and tutorials of my personal blog ! 📝

Aspect Based Sentiment Analysis ⭐ 413

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

Explainx ⭐ 375

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 @ [email protected]

Datascience_artificialintelligence_utils ⭐ 369

Examples of Data Science projects and Artificial Intelligence use-cases

Automated Fact Checking Resources ⭐ 303

Links to conference/journal publications in automated fact-checking (resources for the TACL22/EMNLP23 paper).

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

Shapley ⭐ 198

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

Whitebox ⭐ 184

[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes

Cnn Exposed ⭐ 157

🕵️‍♂️ Interpreting Convolutional Neural Network (CNN) Results.

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

Awesome Explanatory Supervision ⭐ 142

List of relevant resources for machine learning from explanatory supervision

Path_explain ⭐ 134

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

Hierarchical Dnn Interpretations ⭐ 119

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

Xaience ⭐ 100

All about explainable AI, algorithmic fairness and more

User documentation for KServe.

Counterfactualexplanations.jl ⭐ 92

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

Sagemaker Explaining Credit Decisions ⭐ 87

Amazon SageMaker Solution for explaining credit decisions.

Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications

Pytorch_explain ⭐ 80

PyTorch Explain: Logic Explained Networks in Python.

Explainable Machine Learning in Survival Analysis

Deep Explanation Penalization ⭐ 74

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

Treeshap ⭐ 72

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Machine Learning ⭐ 68

Material related to my book Intuitive Machine Learning. Some of this material is also featured in my new book Synthetic Data and Generative AI.

Essential NLP & ML, short & fast pure Python code

Cinnamon ⭐ 66

CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system

Survshap ⭐ 65

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Awesome Shapley Value ⭐ 63

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

Fat Forensics ⭐ 63

Modular Python Toolbox for Fairness, Accountability and Transparency Forensics

Machine Learning For High Risk Applications Book ⭐ 60

Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

R package for SHAP plots

Fast Tsetlin Machine With Mnist Demo ⭐ 52

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

Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.

Xplainable ⭐ 48

Real-time explainable machine learning for business optimisation

Neuro Symbolic Sudoku Solver ⭐ 44

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

Logic_explained_networks ⭐ 43

Logic Explained Networks is a python repository implementing explainable-by-design deep learning models.

Contrastiveexplanation ⭐ 42

Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University

Main folder. Material related to my books on synthetic data and generative AI. Also contains documents blending components from several folders, or covering topics spanning across multiple folders..

Clustershapley ⭐ 40

Explaining dimensionality results using SHAP values

WikiWhy is a new benchmark for evaluating LLMs' ability to explain between cause-effect relationships. It is a QA dataset containing 9000+ "why" question-answer-rationale triplets.

Shapleyexplanationnetworks ⭐ 36

Implementation of the paper "Shapley Explanation Networks"

CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox

Harmonization ⭐ 34

👋 Aligning Human & Machine Vision using explainability

Finding semantically meaningful and accurate prompts.

Model Agnostic Counterfactual Explanations

Kernelshap ⭐ 28

Efficient R implementation of SHAP

Biaslyze ⭐ 28

The NLP Bias Identification Toolkit

Shap_fold ⭐ 28

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

Familiar ⭐ 27

Repository for the familiar R-package. Familiar implements an end-to-end pipeline for interpretable machine learning of tabular data.

Te2rules ⭐ 25

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

Sirus.jl ⭐ 25

Interpretable Machine Learning via Rule Extraction

Disentangled Attribution Curves ⭐ 23

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

This package implements the trustee framework to extract decision tree explanation from black-box ML models.

Sdk Python ⭐ 23

Python library for Modzy Machine Learning Operations (MLOps) Platform

Intel Xai Tools ⭐ 23

Explainable AI Tooling (XAI). XAI is used to discover and explain a model's prediction in a way that is interpretable to the user. Relevant information in the dataset, feature-set, and model's algorithms are exposed.

Open source software for machine learning production monitoring : maintain control over production models, detect bias, explain your results.

GitHub repository for DORA: Data-agnOstic Representation Analysis paper. DORA allows to find outlier representations in Deep Neural Networks.

A Python package with explanation methods for extraction of feature interactions from predictive models

Plaquebox Paper ⭐ 18

Repo for Tang et al, bioRxiv 454793 (2018)

Xai Demonstrator ⭐ 17

The XAI Demonstrator is a modular platform that lets users interact with production-grade Explainable AI (XAI) systems.

Javaanchorexplainer ⭐ 17

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

Explainablemachinelearning 2023 ⭐ 16

eXplainable Machine Learning 2022 at MIM UW

Whitebox Part1 ⭐ 16

In this part, i've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)

Dlime_experiments ⭐ 16

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).

Modeling Uncertainty Local Explainability ⭐ 16

Local explanations with uncertainty 💐!

Awesome Data Science ⭐ 15

Carefully curated list of awesome data science resources.

Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.

Pyexplainer ⭐ 15

PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

Library for generating human-readable machine learning explanations

Cellx Predict ⭐ 15

Explainable AI model of cell behavior

This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.

A Toolbox for the Evaluation of machine learning Explanations

MERLIN is a global, model-agnostic, contrastive explainer for any tabular or text classifier. It provides contrastive explanations of how the behaviour of two machine learning models differs.

Explainableml Vision ⭐ 14

This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.

Machine Learning Finite State Machine Models from Data with Genetic Algorithms

Mindsdb_server ⭐ 12

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

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