wehub-resource-sync
112 lines
3.2 KiB
YAML
112 lines
3.2 KiB
YAML
template:
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bootstrap: 5
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params:
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bootswatch: cerulean
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site:
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root: ''
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title: LightGBM, Light Gradient Boosting Machine
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repo:
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url:
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home: https://github.com/lightgbm-org/LightGBM/
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source: https://github.com/lightgbm-org/LightGBM/tree/main/R-package/
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issue: https://github.com/lightgbm-org/LightGBM/issues/
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user: https://github.com/
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development:
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mode: unreleased
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authors:
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Yu Shi:
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href: https://github.com/shiyu1994
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html: <img src="https://avatars.githubusercontent.com/u/14541765?s=400&v=4" height="48" /> Yu Shi
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Guolin Ke:
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href: https://github.com/guolinke
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html: <img src="https://avatars.githubusercontent.com/u/16040950?s=400&v=4" height="48" /> Guolin Ke
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Damien Soukhavong:
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href: https://github.com/Laurae2
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html: <img src="https://avatars.githubusercontent.com/u/9083669?s=400&v=4" height="48" /> Damien Soukhavong
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Yachen Yan:
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href: https://github.com/yanyachen
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html: <img src="https://avatars.githubusercontent.com/u/6893682?s=400&v=4" height="48" /> Yachen Yan
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James Lamb:
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href: https://github.com/jameslamb
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html: <img src="https://avatars.githubusercontent.com/u/7608904?s=400&v=4" height="48" /> James Lamb
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navbar:
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title: LightGBM
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type: default
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left:
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- icon: fa-reply fa-lg
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href: ../
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- icon: fa-home fa-lg
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href: index.html
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- text: Articles
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href: articles/index.html
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- text: Reference
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href: reference/index.html
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right:
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- icon: fa-github fa-lg
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href: https://github.com/lightgbm-org/LightGBM/tree/main/R-package
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reference:
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- title: Datasets
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desc: Datasets included with the R-package
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contents:
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- '`agaricus.train`'
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- '`agaricus.test`'
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- '`bank`'
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- title: Data Input / Output
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desc: Data I/O required for LightGBM
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contents:
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- '`dim.lgb.Dataset`'
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- '`dimnames.lgb.Dataset`'
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- '`get_field`'
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- '`set_field`'
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- '`lgb.Dataset`'
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- '`lgb.Dataset.construct`'
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- '`lgb.Dataset.create.valid`'
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- '`lgb.Dataset.save`'
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- '`lgb.Dataset.set.categorical`'
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- '`lgb.Dataset.set.reference`'
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- '`lgb.convert_with_rules`'
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- '`lgb.slice.Dataset`'
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- title: Machine Learning
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desc: Train models with LightGBM and then use them to make predictions on new data
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contents:
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- '`lightgbm`'
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- '`lgb.train`'
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- '`predict.lgb.Booster`'
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- '`lgb.cv`'
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- '`lgb.configure_fast_predict`'
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- title: Saving / Loading Models
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desc: Save and load LightGBM models
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contents:
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- '`lgb.dump`'
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- '`lgb.save`'
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- '`lgb.load`'
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- '`lgb.model.dt.tree`'
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- '`lgb.drop_serialized`'
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- '`lgb.make_serializable`'
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- '`lgb.restore_handle`'
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- title: Model Interpretation
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desc: Analyze your models
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contents:
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- '`lgb.get.eval.result`'
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- '`lgb.importance`'
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- '`lgb.interpret`'
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- '`lgb.plot.importance`'
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- '`lgb.plot.interpretation`'
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- '`print.lgb.Booster`'
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- '`summary.lgb.Booster`'
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- title: Multithreading Control
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desc: Manage degree of parallelism used by LightGBM
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contents:
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- '`getLGBMThreads`'
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- '`setLGBMThreads`'
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- title: Deprecated
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desc: Functionality that will be removed in the future
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contents:
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- '`lgb.interprete`'
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