Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/h2oai/mli-resources
H2O.ai Machine Learning Interpretability Resources
accountability data-mining data-science explainable-ml fairness fatml h2o iml interpretability interpretable-ai interpretable-machine-learning interpretable-ml jupyter-notebooks machine-learning machine-learning-interpretability mli python transparency xai xgboost
Last synced: 27 May 2024
https://github.com/explainX/explainx
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]
aws-sagemaker bias blackbox explainability explainable-ai explainable-artificial-intelligence explainable-ml explainx interpretability interpretable-ai interpretable-machine-learning machine-learning machine-learning-interpretability scikit-learn transparency xai
Last synced: 27 May 2024
https://github.com/jphall663/awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
ai-safety awesome awesome-list data-science explainable-ml fairness interpretability interpretable-ai interpretable-machine-learning interpretable-ml machine-learning machine-learning-interpretability privacy-enhancing-technologies privacy-preserving-machine-learning python r reliable-ai secure-ml transparency xai
Last synced: 07 May 2024
https://github.com/12wang3/mllp
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
aaai explainability explainable-ai explainable-ml iml interpretability interpretable-ai interpretable-machine-learning interpretable-ml interpretml machine-learning machine-learning-interpretability rule-based rule-sets transparency transparent-ml xai
Last synced: 18 Apr 2024
https://github.com/SDM-TIB/InterpretME
An interpretable machine learning pipeline over knowledge graphs
interpretability knowledge-graph machine-learning-interpretability machine-learning-models ontologies shacl
Last synced: 18 Apr 2024
https://github.com/jphall663/interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
accountability data-mining data-science decision-tree fairness fatml gradient-boosting-machine h2o iml interpretability interpretable interpretable-ai interpretable-machine-learning interpretable-ml lime machine-learning machine-learning-interpretability python transparency xai
Last synced: 18 Apr 2024