Generalized Boosted Regression Modeling (GBM)

Usage

renoir_gbm(
  x,
  y = NULL,
  weights = NULL,
  offset = NULL,
  resp.type,
  observations = NULL,
  features = NULL,
  clean = FALSE,
  keep.call = TRUE,
  ...,
  eta,
  ntree,
  keep.data = FALSE,
  verbose = F
)

Arguments

x: the input matrix, where rows are observations and columns are variables
y: the response variable. Its number of rows must match the number of rows of x
weights: priors of the observations
offset: used only for GLM methods, it is an a priori known component to be included in the linear predictor during fitting
resp.type: the response type
observations: indices of observations to keep
features: indices of predictors to keep
...: further arguments to gbm.fit
eta: The shrinkage parameter applied to each tree in the expansion. Also known as the learning rate or step-size reduction; 0.001 to 0.1 usually work, but a smaller learning rate typically requires more trees. Default is 0.1.
ntree: the total number of trees to fit. This is equivalent to the number of iterations and the number of basis functions in the additive expansion.
keep.data: logical, whether or not to keep the data and an index of the data stored with the object.
verbose: Logical indicating whether or not to print out progress and performance indicators (TRUE). If this option is left unspecified for gbm.more, then it uses verbose from object. Default is FALSE.
distribution: either a character string specifying the name of the distribution to use or a list with a component name specifying the distribution and any additional parameters needed. If not specified, it is set by resp.type

Value

An object of class Trained

Author

Alessandro Barberis