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cff-version: 1.2.0
message: 'To cite package "stackgbm" in publications use:'
type: software
license: MIT
title: 'stackgbm: Stacked Gradient Boosting Machines'
version: 0.1.0
doi: 10.32614/CRAN.package.stackgbm
abstract: A minimalist implementation of model stacking by Wolpert (1992) <https://doi.org/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.
authors:
- family-names: Xiao
  given-names: Nan
  email: me@nanx.me
  orcid: https://orcid.org/0000-0002-0250-5673
repository: https://nanxstats.r-universe.dev
repository-code: https://github.com/nanxstats/stackgbm
commit: db33de5656579aff009eb5c186378fabe7b5fb1e
url: https://nanx.me/stackgbm/
contact:
- family-names: Xiao
  given-names: Nan
  email: me@nanx.me
  orcid: https://orcid.org/0000-0002-0250-5673