Next week, I’ll be presenting and discussing Statistical/Machine Learning explainability using a **particular** surrogate.

The week after, it will be about explainable ‘AI’ using Gradient Boosted randomized networks. Again!

And the week after: a deeper dive into Generalized (Non)linear models (GNLM) for continuous and discrete outputs in nnetsauce. For a refresher, you can play with this notebook containing examples of GNLMs. Issues/Pull requests, suggestions are welcome, as usual.