Interpretability Libraries

8 Components & Libraries

Sortby

Interpretability Libraries

awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.

A maintained and curated list of practical and awesome responsible machine learning resources. This section is for responsible ML guidance put forward by organizations or individuals, not for officia…

StellarGraph - Machine Learning on Graphs

Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can…

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Effortlessly share and discuss results with non-Data users Deploy interpretability part of your project: From model training to deployment (API or Batch Mode) Shapash is intended to work with Python …

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.

Examples of techniques for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and security. The notebooks highlight techn…

H2O.ai Machine Learning Interpretability Resources

Machine learning algorithms create potentially more accurate models than linear models, but any increase in accuracy over more traditional, better-understood, and more easily explainable techniques i…

A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.

This repository contains an SDK and example Jupyter notebooks to showcase its use. Interpret-Text incorporates community developed interpretability techniques for NLP models and a visualization dashb…

A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)

Simply use:

A Python library for Secure and Explainable Machine Learning

SecML can run with Python >= 3.6 with no additional configuration steps required, as all its dependencies are available as wheel packages for the principal macOS versions, Linux distributions and …

Related tags

natural-language-processing

deep-learning

image-comparison

python