This function trains a regression model for a given X.train
feature matrix, Y.train
response vector, and working parameters. A model returned by this function can be used to predict response for unseen data with RGBM.test
function.
RGBM.train(X.train, Y.train, s_f = 0.3, s_s = 1, lf = 1, M.train = 5000, nu = 0.001)
Input S-by-P feature matrix of training samples. Columns correspond to features, rows correspond to samples.
Input S-element response vector of training samples.
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.
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.
Loss function: 1-> Least Squares and 2 -> Least Absolute Deviation
Number of extensions in boosting model, e.g. number of iterations of the main loop of RGBM algorithm. By default it's 5000.
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.
Regression model is a structure containing all the information needed to predict response for unseen data