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stackgbm  

Stacked Gradient Boosting Machines
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("stackgbm", "0.1.0")



Attach the package and use:
library("stackgbm")
Maintained by
Nan Xiao
[Scholar Profile | Author Map]
First Published: 2024-04-30
Latest Update: 2024-04-30
Description:
A minimalist implementation of model stacking by Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree models. A classic, two-layer stacking model is implemented, where the first layer generates features using gradient boosting trees, and the second layer employs a logistic regression model that uses these features as inputs. Utilities for training the base models and parameters tuning are provided, allowing users to experiment with different ensemble configurations easily. It aims to provide a simple and efficient way to combine multiple gradient boosting models to improve predictive model performance and robustness.
How to cite:
Nan Xiao (2024). stackgbm: Stacked Gradient Boosting Machines. R package version 0.1.0, https://cran.r-project.org/web/packages/stackgbm. Accessed 06 Apr. 2025.
Previous versions and publish date:
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Complete documentation for stackgbm
Functions, R codes and Examples using the stackgbm R package
Full stackgbm package functions and examples
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