gbm_tune: Gradient boosting machine tuning function.

In LBNL-ETA/RMV2.0: M&V2.0 Using R and Shiny

Description

gbm_tune Function used to tune the hyper parameters of the GBM model.

Usage

gbm_tune(Data, k_folds, variables = c("Temp", "tow"), ncores,
  cv_blocks = "none", iter, depth, lr, subsample)

Arguments

Data	A dataframe.
k_folds	An integer that corresponds to the number of CV folds.
variables	A vector that contains the names of the variables that will be considered by the function as input variables.
ncores	Number of threads used for the parallelization of the cross validation
cv_blocks	type of blocks for the cross validation; Default is "none", which corresponds to the standard cross validation technique
iter	A vector with combination of the number of iterations.
depth	A vector with combination of the maximum depths.
lr	A vector with combination of the learning rates.
subsample	A vector with combination of subsamples.

Value

a list with the two following components:

grid_results

a dataframe the training accuracy metrics (R2, RMSE and CVRMSE) and values of the tuning hype-parameters

tuned_parameters

a list of the best hyper-parameters

LBNL-ETA/RMV2.0 documentation built on Nov. 9, 2020, 5:44 a.m.