s_LIHAD: The Linear Hard Hybrid Tree: Hard Additive Tree (no gamma)...

In egenn/rtemis: Machine Learning and Visualization

The Linear Hard Hybrid Tree: Hard Additive Tree (no gamma) with Linear Nodes [R]

Description

Train a Linear Hard Hybrid Tree for Regression

Usage

s_LIHAD(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  max.depth = 3,
  alpha = 0,
  lambda = 0.1,
  lincoef.params = setup.lincoef("glmnet"),
  minobsinnode = 2,
  minobsinnode.lin = 10,
  learning.rate = 1,
  part.minsplit = 2,
  part.xval = 0,
  part.max.depth = 1,
  part.cp = 0,
  weights = NULL,
  metric = "MSE",
  maximize = FALSE,
  grid.resample.params = setup.grid.resample(),
  keep.x = FALSE,
  simplify = TRUE,
  cxrcoef = FALSE,
  n.cores = rtCores,
  verbose = TRUE,
  verbose.predict = FALSE,
  trace = 0,
  x.name = NULL,
  y.name = NULL,
  question = NULL,
  outdir = NULL,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  save.mod = 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
max.depth	[gS] Integer: Max depth of additive tree. Default = 3
alpha	[gS] Float: lincoef alpha Overrides lincoef.params alpha
lambda	[gS] Float: lincoef lambda. Overrides lincoef.params lambda
lincoef.params	Named List: Output of setup.lincoef
minobsinnode	[gS] Integer: Minimum N observations needed in node, before considering splitting
minobsinnode.lin	Integer: Minimum N observations needed in node in order to train linear model.
learning.rate	[gS] Float (0, 1): Learning rate.
part.max.depth	Integer: Max depth for each tree model within the additive tree
part.cp	[gS] Float: Minimum complexity needed to allow split by rpart.
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.
cxrcoef	Logical: Passed to predict.lihad, if TRUE, returns cases by coefficients matrix
verbose	Logical: If TRUE, print summary to screen.
trace	Integer: If higher than 0, will print more information to the console.
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.
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
print.plot	Logical: if TRUE, produce plot using mplot3 Takes precedence over plot.fitted and plot.predicted.
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"
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)

Details

The Hybrid Tree grows a tree using a sequence of regularized linear models and tree stumps. Use s_LINAD for the standard Linear Additive Tree Algorithm, which grows branches stepwise and includes all observations weighted by gamma

Grid searched parameters: max.depth, alpha, lambda, minobsinnode, learning.rate, part.cp

Author(s)

E.D. Gennatas

egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.