boost: Boost an 'rtemis' learner for regression
In egenn/rtemis: Machine Learning and Visualization

boost

R Documentation

Boost an rtemis learner for regression

Description

Train an ensemble using boosting of any learner

Usage

boost(
  x,
  y = NULL,
  x.valid = NULL,
  y.valid = NULL,
  x.test = NULL,
  y.test = NULL,
  mod = "cart",
  resid = NULL,
  boost.obj = NULL,
  mod.params = list(),
  case.p = 1,
  weights = NULL,
  learning.rate = 0.1,
  earlystop.params = setup.earlystop(window = 30, window_decrease_pct_min = 0.01),
  earlystop.using = "train",
  tolerance = 0,
  tolerance.valid = 1e-05,
  max.iter = 10,
  init = NULL,
  x.name = NULL,
  y.name = NULL,
  question = NULL,
  base.verbose = FALSE,
  verbose = TRUE,
  trace = 0,
  print.progress.every = 5,
  print.error.plot = "final",
  prefix = NULL,
  plot.theme = rtTheme,
  plot.fitted = NULL,
  plot.predicted = NULL,
  print.plot = FALSE,
  print.base.plot = FALSE,
  plot.type = "l",
  outdir = NULL,
  ...
)

Arguments

`x`	Numeric vector or matrix / data frame of features i.e. independent variables
`y`	Numeric vector of outcome, i.e. dependent variable
`x.valid`	Data.frame; optional: Validation data
`y.valid`	Float, vector; optional: Validation outcome
`x.test`	Numeric vector or matrix / data frame of testing set features Columns must correspond to columns in `x`
`y.test`	Numeric vector of testing set outcome
`mod`	Character: Algorithm to train base learners, for options, see select_learn. Default = "cart"
`resid`	Float, vector, length = length(y): Residuals to work on. Do not change unless you know what you're doing. Default = NULL, for regular boosting
`boost.obj`	(Internal use)
`mod.params`	Named list of arguments for `mod`
`case.p`	Float (0, 1]: Train each iteration using this perceent of cases. Default = 1, i.e. use all cases
`weights`	Numeric vector: Weights for cases. For classification, `weights` takes precedence over `ifw`, therefore set `weights = NULL` if using `ifw`. Note: If `weight` are provided, `ifw` is not used. Leave NULL if setting `ifw = TRUE`.
`learning.rate`	Float (0, 1] Learning rate for the additive steps
`earlystop.params`	List with early stopping parameters. Set using setup.earlystop
`earlystop.using`	Character: "train" or "valid". For the latter, requires `x.valid`
`tolerance`	Float: If training error <= this value, training stops
`tolerance.valid`	Float: If validation error <= this value, training stops
`max.iter`	Integer: Maximum number of iterations (additive steps) to perform. Default = 10
`init`	Float: Initial value for prediction. Default = mean(y)
`x.name`	Character: Name for feature set
`y.name`	Character: Name for outcome
`question`	Character: the question you are attempting to answer with this model, in plain language.
`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.progress.every`	Integer: Print progress over this many iterations
`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. "none" for no plot.
`prefix`	Internal
`plot.theme`	Character: "zero", "dark", "box", "darkbox"
`plot.fitted`	Logical: if TRUE, plot True (y) vs Fitted
`plot.predicted`	Logical: if TRUE, plot True (y.test) vs Predicted. Requires `x.test` and `y.test`
`print.plot`	Logical: if TRUE, produce plot using `mplot3` Takes precedence over `plot.fitted` and `plot.predicted`.
`print.base.plot`	Logical: Passed to `print.plot` argument of base learner, i.e. if TRUE, print error plot for each base learner
`plot.type`	Character: "l" or "p". Plot using lines or points.
`outdir`	Path to output directory. If defined, will save Predicted vs. True plot, if available, as well as full model output, if `save.mod` is TRUE
`...`	Additional parameters to be passed to learner define by `mod`