gbts: Hyperparameter Search for Gradient Boosted Trees

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.

AuthorWaley W. J. Liang
Date of publication2016-10-17 01:03:17
MaintainerWaley W. J. Liang <wliang10@gmail.com>
LicenseGPL (>= 2) | file LICENSE
Version1.0.1

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Files in this package

gbts
gbts/tests
gbts/tests/testthat.R
gbts/tests/testthat
gbts/tests/testthat/test-utility-acc.R
gbts/tests/testthat/test-utility-comperf.R
gbts/tests/testthat/test-utility-auc.R
gbts/tests/testthat/test-utility-ks.R
gbts/tests/testthat/test-utility-mae.R
gbts/tests/testthat/test-utility-dev.R
gbts/tests/testthat/test-utility-roc.R
gbts/tests/testthat/test-utility-mse.R
gbts/tests/testthat/test-utility-rsq.R
gbts/NAMESPACE
gbts/NEWS.md
gbts/data
gbts/data/boston_housing.RData
gbts/data/german_credit.RData
gbts/R
gbts/R/gbt.R gbts/R/gbts.R gbts/R/data.R gbts/R/utility.R
gbts/README.md
gbts/MD5
gbts/DESCRIPTION
gbts/man
gbts/man/german_credit.Rd gbts/man/comperf.Rd gbts/man/predict.gbts.Rd gbts/man/gbts.Rd gbts/man/boston_housing.Rd
gbts/LICENSE

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