s_LIHADBoost: Boosting of Linear Hard Additive Trees [R]

In egenn/rtemis: Machine Learning and Visualization

Boosting of Linear Hard Additive Trees [R]

Description

Boost a Linear Hard Additive Tree (i.e. LIHAD, i.e. LINAD with hard splits)

Usage

s_LIHADBoost(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  resid = NULL,
  boost.obj = NULL,
  learning.rate = 0.5,
  case.p = 1,
  max.depth = 5,
  gamma = 0.1,
  alpha = 0,
  lambda = 1,
  lambda.seq = NULL,
  minobsinnode = 2,
  minobsinnode.lin = 10,
  shrinkage = 1,
  part.minsplit = 2,
  part.xval = 0,
  part.max.depth = 1,
  part.cp = 0,
  part.minbucket = 5,
  lin.type = c("glmnet", "cv.glmnet", "lm.ridge", "allSubsets", "forwardStepwise",
    "backwardStepwise", "glm", "sgd", "solve", "none"),
  cv.glmnet.nfolds = 5,
  which.cv.glmnet.lambda = "lambda.min",
  max.iter = 10,
  tune.n.iter = TRUE,
  earlystop.params = setup.earlystop(),
  lookback = TRUE,
  init = NULL,
  .gs = FALSE,
  grid.resample.params = setup.resample("kfold", 5),
  gridsearch.type = "exhaustive",
  metric = NULL,
  maximize = NULL,
  cxrcoef = FALSE,
  print.progress.every = 5,
  print.error.plot = "final",
  x.name = NULL,
  y.name = NULL,
  question = NULL,
  base.verbose = FALSE,
  verbose = TRUE,
  grid.verbose = FALSE,
  trace = 0,
  prefix = NULL,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  print.plot = FALSE,
  print.base.plot = FALSE,
  print.tune.plot = TRUE,
  plot.type = "l",
  save.gridrun = FALSE,
  outdir = NULL,
  n.cores = rtCores,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
  ...
)

Arguments

x	Numeric vector or matrix / data frame of features i.e. independent variables
y	Numeric vector of outcome, i.e. dependent variable
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
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)
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
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
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
plot.theme	Character: "zero", "dark", "box", "darkbox"
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
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
save.mod	Logical: If TRUE, save all output to an RDS file in outdir save.mod is TRUE by default if an outdir is defined. If set to TRUE, and no outdir is defined, outdir defaults to paste0("./s.", mod.name)
...	Additional parameters to be passed to learner

Details

By default, early stopping works by checking training loss.

Author(s)

E.D. Gennatas

egenn/rtemis documentation built on Oct. 28, 2024, 6:30 a.m.