A growing collection of resources about Machine Learning eXplainability. Our objective is to cover both theory and practice, including:
- high-level overviews & introductory examples;
- mathematical foundations;
- algorithmic implementations;
- practical advice & real-life caveats; and
- success & failure case studies.
We will be adding more eXplainable Machine Learning techniques to our compendium over time, so stay tuned. Cannot wait? Contribute a technique description yourself.