fdm2id (version 1.0.1)

GRADIENTBOOSTING: Classification using Gradient Boosting

Description

This function builds a classification model using Gradient Boosting

Usage

GRADIENTBOOSTING(
  train,
  labels,
  ntree = 500,
  learningrate = 0.3,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Value

The classification model.

Arguments

train

The training set (description), as a data.frame.

labels

Class labels of the training set (vector or factor).

ntree

The number of trees in the forest.

learningrate

The learning rate (between 0 and 1).

tune

If true, the function returns parameters instead of a classification model.

methodparameters

Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: GRADIENTBOOSTING does not yet implement hyperparameter tuning.

graph

Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: GRADIENTBOOSTING does not produce a plot.

seed

A specified seed for random number generation (row/column subsampling, if used via ...; xgboost's default parameters are otherwise deterministic, but the seed is provided for consistency with the rest of the package's API and to cover subsampling parameters passed through ...).

...

Other parameters.

See Also

xgboost

Examples

# \donttest{
require (datasets)
data (iris)
GRADIENTBOOSTING (iris [, -5], iris [, 5])
# }