| GBM.train {RGBM} | R Documentation |
Train GBM predictor
Description
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 GBM.test function.
Usage
GBM.train(X.train, Y.train, s_f = 0.3, s_s = 1, lf =1, M.train = 5000, nu = 0.001)
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. |
Value
Regression model is a structure containing all the information needed to predict response for unseen data
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>