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  "Title": "Stacked Gradient Boosting Machines",
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  "Authors@R": "c(\nperson(\"Nan\", \"Xiao\", email = \"me@nanx.me\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-0250-5673\")))",
  "Description": "A minimalist implementation of model stacking by Wolpert\n(1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree\nmodels. A classic, two-layer stacking model is implemented,\nwhere the first layer generates features using gradient\nboosting trees, and the second layer employs a logistic\nregression model that uses these features as inputs. Utilities\nfor training the base models and parameters tuning are\nprovided, allowing users to experiment with different ensemble\nconfigurations easily. It aims to provide a simple and\nefficient way to combine multiple gradient boosting models to\nimprove predictive model performance and robustness.",
  "License": "MIT + file LICENSE",
  "URL": "https://nanx.me/stackgbm/, https://github.com/nanxstats/stackgbm",
  "BugReports": "https://github.com/nanxstats/stackgbm/issues",
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  "Repository": "https://nanxstats.r-universe.dev",
  "Date/Publication": "2024-05-01 02:31:22 UTC",
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