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gbts  

Hyperparameter Search for Gradient Boosted Trees
View on CRAN: Click here


Download and install gbts package within the R console
Install from CRAN:
install.packages("gbts")

Install from Github:
library("remotes")
install_github("cran/gbts")

Install by package version:
library("remotes")
install_version("gbts", "1.2.0")



Attach the package and use:
library("gbts")
Maintained by
Waley W. J. Liang
[Scholar Profile | Author Map]
First Published: 2016-10-08
Latest Update: 2017-02-27
Description:
An implementation of hyperparameter optimization for Gradient Boosted Trees on binary classification and regression problems. The current version provides two optimization methods: Bayesian optimization and random search. Instead of giving the single best model, the final output is an ensemble of Gradient Boosted Trees constructed via the method of ensemble selection.
How to cite:
Waley W. J. Liang (2016). gbts: Hyperparameter Search for Gradient Boosted Trees. R package version 1.2.0, https://cran.r-project.org/web/packages/gbts. Accessed 30 Apr. 2025.
Previous versions and publish date:
1.0.0 (2016-10-08 18:55), 1.0.1 (2016-10-17 01:03)
Other packages that cited gbts R package
View gbts citation profile
Other R packages that gbts depends, imports, suggests or enhances
Complete documentation for gbts
Functions, R codes and Examples using the gbts R package
Some associated functions: boston_housing . comperf . gbts . german_credit . predict.gbts . 
Some associated R codes: data.R . gbts.R . utility.R .  Full gbts package functions and examples
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