We just open-sourced our mlflow plugin to generate html reports that let you interactively explore shap values. We're happy for any feedback. Feel free to ask here or submit issues to the repo. |
We just open-sourced our mlflow plugin to generate html reports that let you interactively explore shap values. We're happy for any feedback. Feel free to ask here or submit issues to the repo. |
- There is already a mlflow builtin tool to log shap plots. This is quite helpful but becomes tedious if you want to dive deep into explainability, e.g. if you want to understand the influence factors for 100s of observations. Furthermore they lack interactivity. Here's the link to the builtin tool: https://mlflow.org/docs/latest/ml/evaluation/shap
- There are tools like shapash or what-if tool, but those require a running python environment. This plugin let's you log shap values in any productive run and explore them in pure html, with some of the features that the other tools provide (more might be coming if we see interest in this).
Here are the links: what-if tool (though it's not actively maintained): https://github.com/PAIR-code/what-if-tool
shapash: https://github.com/MAIF/shapash