cartLiteBoostTV: Boost an 'rtemis' learner for regression

In egenn/rtemis: Machine Learning and Visualization

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

Details

If learning.rate is set to 0, a nullmod will be created

Author(s)

E.D. Gennatas

egenn/rtemis documentation built on Dec. 17, 2024, 6:16 p.m.