Description Usage Arguments Value
Training a gradient boosting machine
1 2 3 4 5 6 | 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)
|
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). |
return a list having four objects: models pred_method train_rmse test_rmse
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