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

cartLiteBoostTV

R Documentation

Boost an rtemis learner for regression

Description

Perform regression by boosting a base learner

Usage

cartLiteBoostTV(
  x,
  y = NULL,
  x.valid = NULL,
  y.valid = NULL,
  x.test = NULL,
  y.test = NULL,
  resid = NULL,
  boost.obj = NULL,
  mod.params = list(),
  weights.p = 1,
  weights.0 = 0,
  weights = NULL,
  learning.rate = 0.1,
  max.iter = 10,
  init = NULL,
  seed = 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
`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 `cartLite`
`weights.p`	Float (0, 1]: Percent of weights to set to 1, the rest will be set to `weights.0`. Default = 1
`weights.0`	Float (0, 1): Set weights of excluded cases to this number. Default = 0, which is equivalent to excluding them, in which case, these cases can act as a validation set
`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
`max.iter`	Integer: Maximum number of iterations (additive steps) to perform. Default = 10
`init`	Float: Initial value for prediction. Default = mean(y)
`seed`	Integer: Set seed to allow reproducibility when `weights.p` is not 1
`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. Default = "final"
`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. Default = FALSE
`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 `cartLite`