Fit interpretable models. Explain blackbox machine learning.
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Updated
Aug 19, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
Shapley Interactions for Machine Learning
On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness
This repository includes a machine learning modeling study about estimating customers hotel cancellation and what are the reasons for these cancellations.
An awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences.
Interpretable Machine Learning via Rule Extraction
"XRec: Large Language Models for Explainable Recommendation"
SHAP Plots in R
Code and data of the paper "Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales," NAACL Findings 2024
A repo containing all the material from the Laboratory Sessions of the Deep Learning Applications course, held by Professor Andrew David Bagdanov (bagdanov) at the University of Florence, Italy.
A curated list of resources on Explainable AI (XAI) in Remote Sensing, including courses, papers, datasets, and repositories.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
User documentation for KServe.
Different SHAP algorithms
Concise summaries of key papers in responsible AI.
High-Performance Symbolic Regression in Python and Julia
Explores diabetes prediction using various ML models and XAI techniques (SHAP, LIME, ALE) on the Pima Indian Diabetes dataset.
Explainable AI for Image Classification
A collection of research materials on explainable AI/ML
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
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