gbm_train: Gradient boosting machine based on GPR

In weichunliao/baeirGPR: Traing a Gaussian Process Regression and Conduct Predictions.

Description

Training a gradient boosting machine

Usage

gbm_train(train_x, train_y, test_x, test_y, pred_method = "2",
  n_model = 500, batch_size = 1000, lr = 0.1, decay_lr = 1,
  tune_param = FALSE, tune_size = NULL, sr_size = NULL,
  selected_n_feature = NULL, update_kparam_tiems = 50,
  update_col_sample = 50, update_lr = 50, kname = "gaussiandotrel",
  ktheta = NULL, kbetainv = NULL, ncpu = -1)

Arguments

train_x	Matrix; the features of training data set.
train_y	Matrix; y.
pred_method	String; Set the model training approach. 1: random row sampling after all training data have been used. 2: random row sampling. 3: row sampling plus col sampling.
n_model	Positive number of submodel in gbm.
batch_size	Positive integer; batch size for each iteration.
lr	Numeric between 0-1; learning rate.
decay_lr	Numeric between 0-1; decay learning rate, default = 1 (no decay).
tune_param	Boolean; Set to TRUE to tune parameters of kernel function default value is TRUE.
tune_size	Positve integer; size of tuning data set.
sr_size	Positive integer; size of sub dataset in gbm_sr
update_col_sample	Positive integer; time to update kernel parameter(for method 3).
update_lr	Positive integer; time to decay learning rate; default is 50.
kname	String; the name of kernel; default value is 'gaussiandotrel'.
ktheta	Numeric vector; store kernel parameter; should be provided when tune_param is FALSE.
kbetainv	Numeric; store kernel parameter betainv; shuld be provided when tune_param is FALSE.
ncpu	Integer; the number of thread to be used; set to -1 to use all threads; default value is -1.
tune_param	Boolean; Set to TRUE to tune parameters of kernel function, default value for pred_method 1 & 2 is TRUE. default value for pred_method 3 is FALSE.
update_kparam_times	Positve integer; time to update kernel parameter(for method 1/2).

Value

return a list having four objects: models pred_method train_rmse test_rmse

weichunliao/baeirGPR documentation built on May 18, 2019, 9:15 p.m.