daisugi

daisugi: not-so-well-known tree machines daisugi is an R package collecting experimental, obscure, and emerging tree-based machine learning methods. Rather than reproducing the mainstream boosting ecosystem, daisugi focuses on: probabilistic forests hybrid boosting systems online learners interpretable ensembles experimental tree architectures research-oriented methods rarely exposed to R users Popular libraries such as XGBoost, LightGBM, and CatBoost are intentionally excluded and are better accessed through their native packages or through tidymodels infrastructure. Included Machines v0.0.5 Status Algorithm Focus ✅ Boulevard stochastic gradient boosting ✅ Conditional Trees unbiased recursive partitioning ✅ EBM interpretable additive boosting ✅ Evolutionary Trees genetic tree optimization 🚧 FairGBM fairness-aware boosting 🚧 GRANDE differentiable tree ensembles 🚧 KTBoost kernel-tree hybrid boosting 🚧 MorphBoost adaptive boosting structures 🚧 MSBoost multi-stage boosting ✅ NGBoost natural gradient prediction ✅ NRGBoost energy-based generative boosting ✅ Perpetual continual tree learning ✅ SnapBoost heterogeneous boosting systems ✅ WildWood randomized online forests ✅ Yggdrasil scalable tree ecosystems Installation You can install the development version of daisugi from GitHub: # install.packages("pak") pak::pak("frankiethull/daisugi") Philosophy daisugi explores tree systems outside the conventional gradient boosting canon. Many included methods emphasize: uncertainty estimation heterogeneous base learners recursive partition hybrids online adaptation probabilistic outputs alternative split mechanics interpretable ensemble structures The package acts as both: a practical modeling toolkit a curated collection of unconventional tree algorithms Example library(daisugi) model <- grow_yggdrasil_trees( x = iris[, 1:4], y = iris$Species ) harvest_yggdrasil_trees(model, iris[, 1:4]) Further Documentation Get Started with a Classification Task Learn more about daisugi via the Glossary. A full list of models & their methods can be found in the Package Index Notes on each verstion at the Changelog