Berent Å. S. Lunde

Implement Shapley-values to explain model predictions. See vignett for https://github.com/NorskRegnesentral/shapr

Currently `agtboost` only supports a purely numerical design-matrix. Consider letting `agtboost` handle categorical features (`factor` variable in R) internally. **Benefits** - Easier interface for users - Could in principle support...

See xgboost implementation. This would be easy if gradient-calculations was on the R-side. But perhaps harder since `agtboost` gradient calculations happen on R-side. **Suggestions** - Take a look if this...

- youtube-video? - viginette? - More information in README? - Github docs - tutorial with "how-it-works" and examples?

Secret manuscript will arrive on arxiv first In principle: - derivatives are influenced by their own response (self-influence) - employ asymptotic correspondance between n-fold CV, influence adjustment and TIC

Secret manuscript will be uploaded to arxiv

Secret manuscript will be uploaded to arxiv

Overall, codebase, especially on the C++ side, needs cleanup in terms of - Commented code - Style-guide - Verbosity Major revision would include removing different variants of count-regression except for...

Add sparsity and agtboost matrix class to hold pointer to C++ design matrix and response vector

1

Should be possible with the Eigen sparse matrix class + R Matrix package and possible RcppModules to return pointer to C++ model object.

Add paralellisation with `OpenMP`

2

The `node::split_information()`should be easy to paralellize.