XAI (eXplainable artificial intelligence) is a fast growing and super interesting area.
Working with complex models generates lots of problems with model validation (on test data performance is great but drops at production), model bias, lack of stability and many others. We need more than just local explanations for predictive models.

The more complex are models the better tools are needed to understand how models are working, explore model behaviour and debug potential errors.

Two years ago I’ve initiated work on the DALEX package. Library packed with functions for local and global model exploration.
Over the time the package went through few architectural changes and now it is part of a larger universe of tools for model exploration developed at MI2DataLab with an increasing support of external contributors (join us).

To explain our philosophy behind the model exploration we (together with Tomasz Burzykowski from Hasselt) started a book ,,Predictive Models: Explore, Explain, and Debug’’.

First part, devoted to local exploration, is ready to read. It explains how to use DALEX with iBreakDown and ingredients packages for instance level explanations.
Later we will describe other packages from our universe.

Find the book-down version of here.

Find a one-page-cheatsheet here.

Let us improve these descriptions by adding pull requests or issues at the GitHub repo.
One day there will be a paper version