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. Instead of giving the single best model, the final output is an ensemble of Gradient Boosted Trees constructed via the method of ensemble selection.
|Author||Waley W. J. Liang|
|Date of publication||2017-02-27 08:41:32|
|Maintainer||Waley W. J. Liang <[email protected]>|
|License||GPL (>= 2) | file LICENSE|
|Package repository||View on CRAN|
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