Arguments
X.train
Input N-by-p feature matrix of training samples. Columns correspond to features, rows correspond to samples.
Y.train
Input N-element response vector of training samples.
s_f
Sampling rate of features, 0<s_f<=1. Fraction of columns from X.train, which will be sampled without replacement to calculate each extesion in boosting model. By default it's 0.3.
s_s
Sampling rate of samples, 0<s_s<=1. Fraction of rows from X.train, which will be sampled with replacement to calculate each extension in boosting model. By default it's 1.
lf
Loss function: 1-> Least Squares and 2 -> Least Absolute Deviation
M.train
Number of extensions in boosting model, e.g. number of iterations of the main loop of RGBM algorithm. By default it's 5000.
nu
Shrinkage factor, learning rate, 0<nu<=1. Each extension to boosting model will be multiplied by the learning rate. By default it's 0.001.