Fit interpretable models. Explain blackbox machine learning.
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Updated
May 28, 2024 - C++
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 game theoretic approach to explain the output of any machine learning model.
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
Influence Estimation for Gradient-Boosted Decision Trees
Logging component of the multi-level explainability framework for multi-agent BDI systems
The narrative generator component of the multi-level explainability framework for BDI multi-agent systems
ES-HyperNEAT Python implementation with C++ computations for NeuroEvolution, Reinforcement Learning and VfMRI
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Python framework for interpretable protein prediction
Papers about explainability of GNNs
TrustyAI Explainability Toolkit
Brain age prediction and networks explainability on their decision
Concise summaries of key papers in responsible AI.
🗺️ Data Cleaning and Textual Data Visualization 🗺️
This Tower of Hanoi Game Prototype is an attempt at explaining Q-learning and Reinforcement Learning Principles to People in an Intuitive and Interactive Way
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.
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