rtemis (version 0.79)

addtboost: rtemis internal: Gradient Boosting of Additive Trees

Description

Boosted additive trees. This is lower-level than s.* functions

Usage

addtboost(x, y, x.valid = NULL, y.valid = NULL, resid = NULL,
  boost.obj = NULL, mod.params = list(), case.p = 1,
  learning.rate = 0.1, max.iter = 10, init = mean(y),
  cxrcoef = FALSE, print.progress.every = 5,
  print.error.plot = "final", base.verbose = FALSE, verbose = TRUE,
  trace = 0, prefix = NULL, print.plot = TRUE,
  plot.theme = "darkgrid", plot.type = "l", ...)

Arguments

x

Data frame: Input features

y

Vector: Output

mod.params

Named list of arguments for mod

learning.rate

Float (0, 1] Learning rate for the additive steps

max.iter

Integer: Maximum number of iterations (additive steps) to perform. Default = 10

init

Float: Initial value for prediction. Default = mean(y)

cxrcoef

Logical: If TRUE, pass cxr = TRUE, cxrcoef = TRUE to predict.addTreeRaw

print.error.plot

String or Integer: "final" plots a training and validation (if available) error curve at the end of training. If integer, plot training and validation error curve every this many iterations during training for each base learner

base.verbose

Logical: verbose argument passed to learner

verbose

Logical: If TRUE, print summary to screen.

trace

Integer: If > 0, print diagnostic info to console

print.plot

Logical: if TRUE, produce plot using mplot3 Takes precedence over plot.fitted and plot.predicted

plot.theme

String: "zero", "dark", "box", "darkbox"

...

Additional parameters to be passed to learner

mod

Algorithm to boost, for options, see modSelect

tolerance

Float: If training error <= this value, training stops

tolerance.valid

Float: If validation error <= this value, training stops

Value

addtboost object