Skip to content
#

boosting-additive-models

Star

Here is 1 public repository matching this topic...

tk-yasuno / tabgam_additive

Star 0

This study proposes a diverse family of additive models that extend Generalized Additive Models to achieve both interpretability and predictive performance in forecasting. While preserving the stability of traditional GAMs, we develop Feature-wise Additive Models, Gradient Boosting Additive Models, and TabNet-based Neural Additive Models (TabNAM).

forecasting ensemble-learning generalized-additive-models interpretable-ml explainable-ai tabnet tabular-deep-learning neural-additive-models tabnam boosting-additive-models feature-wise-additive-models
  • Updated Mar 15, 2026
  • Python

Improve this page

Add a description, image, and links to the boosting-additive-models topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the boosting-additive-models topic, visit your repo's landing page and select "manage topics."

Learn more