GradientBoost: Fit a gradient boosted ensemble of underlying tree or...

In 19900321/flipMultivariates: Multivariate models

Description

Fit a gradient boosted ensemble of underlying tree or regression models

Usage

GradientBoost(formula, data = NULL, subset = NULL, weights = NULL,
  output = "Accuracy", missing = "Exclude cases with missing data",
  booster = "gbtree", grid.search = FALSE, seed = 12321,
  show.labels = FALSE)

Arguments

formula	A formula of the form groups ~ x1 + x2 + ... That is, the response is the grouping factor and the right hand side specifies the (non-factor) discriminators, and any transformations, interactions, or other non-additive operators apart from . will be ignored.
data	A data.frame from which variables specified in formula are preferentially to be taken.
subset	An optional vector specifying a subset of observations to be used in the fitting process, or, the name of a variable in data. It may not be an expression.
weights	An optional vector of sampling weights, or the name of a variable in data. It may not be an expression.
output	One of "Accuracy", "Importance", "Prediction-Accuracy Table" or "Detail".
missing	How missing data is to be treated. Options: "Error if missing data", "Exclude cases with missing data", or "Imputation (replace missing values with estimates)".
booster	Whether the underlying model is a tree or linear regression. Options: "gbtree", "gblinear".
grid.search	Whether to search the parameter space in order to tune the model.
seed	The random number seed.
show.labels	Shows the variable labels, as opposed to the labels, in the outputs, where a variables label is an attribute (e.g., attr(foo, "label")).

19900321/flipMultivariates documentation built on May 29, 2019, 8:33 a.m.