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LIME and SHAP are two very popular methods for instance level explanations of machine learning models (XAI).
They work nicely for images and text inputs, but share similar weakness in case of tabular data: explanations are additive while complex models are (sometimes) not. iBreakDown addresses this problem.
– It identifies and shows feature interactions (if there are local interactions in the model).
– It is much faster. For additive explanations the complexity is O(p) instead of O(p^2).
– The plotD3 function creates an interactive D3-based break-down plot (thanks to r2d3).
– iBreakDown has a new design, created by Hanna Dyrcz. We will have a talk about it ,,Machine learning meets design. Design meets machine learning.” at satRdays. Try the new theme
– It shows explanation level uncertainty – how good are explanations?
A methodology behind this package is described in the iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models.
A nice titanic-powered use-case is described in the titanic vignette.
An example of the D3 interactive explainer is here.
Some intuition is introduced in the Visual Exploration, Explanation and Debugging (working version, still in progress).
Let us know if you like it. Feel free to create a pull request with new features, add issue with new idea or star the github repository if you like this package.