gratia

An extension of XGBoost to probabilistic modelling

An extension of LightGBM to probabilistic modelling

Boosting algorithms for fitting generalized linear, additive and interaction models to potentially high-dimensional data. The current relase version can be found on CRAN (http://cran.r-project.org/package=mboost).

Distributional Gradient Boosting Machines

Generalized Additive Models in Python.

Code for the KDD 2019 workshop paper. Attention mechanism for distribution regression.

Sintaks yang digunakan pada skripsi yang berjudul Pemodelan Kerugian pada Asuransi Kendaraan Bermotor Menggunakan Generalized Linear Models dan Generalized Additive Models.

My scripts from BL5233 lectures and practicals.

This project explores additive models, mixed models (LMM, GAMM), and GLM to analyze and predict pig growth based on age, chest size, and environmental factors (farm conditions, species, etc.).