A game theoretic approach to explain the output of any machine learning model.
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Updated
Jun 2, 2024 - Jupyter Notebook
A game theoretic approach to explain the output of any machine learning model.
Fit interpretable models. Explain blackbox machine learning.
Implementation of "Explaining cube measures through Intentional Analytics." @ Information Systems (2024). DOI: https://doi.org/10.1016/j.is.2023.102338
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Explaining black boxes with a SMILE: Statistical Mode-agnostic Interpretability with Local Explanations
GNN Explainability in a regression setting - semester project for Applied Mathematics MSc @ EPFL
Papers about explainability of GNNs
The narrative generator component of the multi-level explainability framework for BDI multi-agent systems
Repository for the paper 'CausalConceptTS: Causal Attributions for Time Series Classification using High Fidelity Diffusion Models'.
Explain model and feature dependencies by decomposition of SHAP values
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
Interpretable text embeddings by asking LLMs yes/no questions
For calculating global feature importance using Shapley values.
Influence Estimation for Gradient-Boosted Decision Trees
Logging component of the multi-level explainability framework for multi-agent BDI systems
ES-HyperNEAT Python implementation with C++ computations for NeuroEvolution, Reinforcement Learning and VfMRI
Python framework for interpretable protein prediction
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
TrustyAI Explainability Toolkit
GraphXAI: Resource to support the development and evaluation of GNN explainers
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