sym.gbm: Generalized Boosted Symbolic Regression

Description

Generalized Boosted Symbolic Regression

Usage

sym.gbm(
  formula,
  sym.data,
  method = c("cm", "crm"),
  distribution = "gaussian",
  interaction.depth = 1,
  n.trees = 500,
  shrinkage = 0.1
)

Arguments

formula: A symbolic description of the model to be fit. The formula may include an offset term (e.g. y~offset(n)+x). If keep.data = FALSE in the initial call to gbm then it is the user's responsibility to resupply the offset to gbm.more.
sym.data: symbolic data table
method: cm crm
distribution: distribution
interaction.depth: Integer specifying the maximum depth of each tree (i.e., the highest level of variable interactions allowed). A value of 1 implies an additive model, a value of 2 implies a model with up to 2-way interactions, etc. Default is 1.
n.trees: Integer specifying 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. Default is 100.
shrinkage: A 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.

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38