Fit interpretable models. Explain blackbox machine learning.
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Updated
Aug 19, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
A game theoretic approach to explain the output of any machine learning model.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Classification model using Gradient Goosted Trees with feature selection and explainability
Concise summaries of key papers in responsible AI.
Evaluating ChatGPT’s Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness
TrustyAI Explainability Toolkit
This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.
This projects aims to classify potential churn customers using a Telco Customer Dataset from IBM. The main applications are about the explainability integration with SHAP algorithm and the creation of an interactive dashboard with Exploratory, Classification and Explainability insights.
A Graph Neural Network-based Intrusion Detection System
Implementation of Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning (Kohler, Delfosse, et. al. 2024).
Official implementation of the paper "MambaLRP: Explaining Selective State Space Sequence Models".
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.
For calculating global feature importance using Shapley values.
Service to examine data processing pipelines (e.g., machine learning or deep learning pipelines) for uncertainty consistency (calibration), fairness, and other safety-relevant aspects.
Benchmark to Evaluate EXplainable AI
Implementation of "Explaining cube measures through Intentional Analytics." @ Information Systems (2024). DOI: https://doi.org/10.1016/j.is.2023.102338
The repository contains code for the project "Explain the uncertain: Stochastic Shapley Values for Gaussian Process Models"
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
A list of (post-hoc) XAI for time series
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