Displaying 1 to 7 from 7 results

shapash - 🔅 Shapash makes Machine Learning models transparent and understandable by everyone

  •    Jupyter

Shapash is a Python library which aims to make machine learning interpretable and understandable by everyone. It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can understand their models easily and share their results. End users can understand the decision proposed by a model using a summary of the most influential criteria.

xai - XAI - An eXplainability toolbox for machine learning

  •    Python

XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning. You can find the documentation at https://ethicalml.github.io/xai/index.html. You can also check out our talk at Tensorflow London where the idea was first conceived - the talk also contains an insight on the definitions and principles in this library.

contextual-ai - Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users

  •    Jupyter

Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users. In this simple example, we will attempt to generate explanations for some ML model trained on 20newsgroups, a text classification dataset. In particular, we want to find out which words were important for a particular prediction.




trusty-ai-sandbox - A sandbox repository for the Trusty AI team

  •    Jupyter

Example of a Jupyter notebook that shows how to train a model and export it as PMML to be integrated then to DMN (link). The notebook has been created starting from risk-pmml-builder example.

GNNLens2 - Visualization tool for Graph Neural Networks

  •    TypeScript

GNNLens2 is an interactive visualization tool for graph neural networks (GNN). It allows seamless integration with deep graph library (DGL) and can meet your various visualization requirements for presentation, analysis and model explanation. It is an open source version of GNNLens with simplification and extension. A video demo is available here. Switch the video quality for the best viewing experience.






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