s_CART: Classification and Regression Trees [C, R, S]
In egenn/rtemis: Machine Learning and Visualization

s_CART

R Documentation

Classification and Regression Trees [C, R, S]

Description

Train a CART for regression or classification using rpart

Usage

s_CART(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  x.name = NULL,
  y.name = NULL,
  weights = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  method = "auto",
  parms = NULL,
  minsplit = 2,
  minbucket = round(minsplit/3),
  cp = 0.01,
  maxdepth = 20,
  maxcompete = 0,
  maxsurrogate = 0,
  usesurrogate = 2,
  surrogatestyle = 0,
  xval = 0,
  cost = NULL,
  model = TRUE,
  prune.cp = NULL,
  use.prune.rpart.rt = TRUE,
  return.unpruned = FALSE,
  grid.resample.params = setup.resample("kfold", 5),
  gridsearch.type = c("exhaustive", "randomized"),
  gridsearch.randomized.p = 0.1,
  save.gridrun = FALSE,
  metric = NULL,
  maximize = NULL,
  n.cores = rtCores,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  grid.verbose = verbose,
  outdir = NULL,
  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
`x.name`	Character: Name for feature set
`y.name`	Character: Name for outcome
`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`.
`ifw`	Logical: If TRUE, apply inverse frequency weighting (for Classification only). Note: If `weights` are provided, `ifw` is not used.
`ifw.type`	Integer 0, 1, 2 1: class.weights as in 0, divided by min(class.weights) 2: class.weights as in 0, divided by max(class.weights)
`upsample`	Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Note: upsample will randomly sample with replacement if the length of the majority class is more than double the length of the class you are upsampling, thereby introducing randomness
`downsample`	Logical: If TRUE, downsample majority class to match size of minority class
`resample.seed`	Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)
`method`	Character: "auto", "anova", "poisson", "class" or "exp".
`parms`	List of additional parameters for the splitting function. See `rpart::rpart("parms")`
`minsplit`	[gS] Integer: Minimum number of cases that must belong in a node before considering a split.
`minbucket`	[gS] Integer: Minimum number of cases allowed in a child node.
`cp`	[gS] Float: Complexity threshold for allowing a split.
`maxdepth`	[gS] Integer: Maximum depth of tree.
`maxcompete`	Integer: The number of competitor splits saved in the output
`maxsurrogate`	Integer: The number of surrogate splits retained in the output (See `rpart::rpart.control`).
`usesurrogate`	See `rpart::rpart.control`
`surrogatestyle`	See `rpart::rpart.control`
`xval`	Integer: Number of cross-validations
`cost`	Vector, Float (> 0): One for each variable in the model. See `rpart::rpart("cost")`
`model`	Logical: If TRUE, keep a copy of the model.
`prune.cp`	[gS] Numeric: Complexity for cost-complexity pruning after tree is built
`use.prune.rpart.rt`	(Testing only, do not change)
`return.unpruned`	Logical: If TRUE and `prune.cp` is set, return unpruned tree under `extra` in `rtMod`.
`grid.resample.params`	List: Output of setup.resample defining grid search parameters.
`gridsearch.type`	Character: Type of grid search to perform: "exhaustive" or "randomized".
`gridsearch.randomized.p`	Float (0, 1): If `gridsearch.type = "randomized"`, randomly test this proportion of combinations.
`save.gridrun`	Logical: If TRUE, save grid search models.
`metric`	Character: Metric to minimize, or maximize if `maximize = TRUE` during grid search. Default = NULL, which results in "Balanced Accuracy" for Classification, "MSE" for Regression, and "Coherence" for Survival Analysis.
`maximize`	Logical: If TRUE, `metric` will be maximized if grid search is run.
`n.cores`	Integer: Number of cores to use.
`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"
`question`	Character: the question you are attempting to answer with this model, in plain language.
`verbose`	Logical: If TRUE, print summary to screen.
`grid.verbose`	Logical: Passed to `gridSearchLearn`
`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)`

Details

[gS] indicates grid search will be performed automatically if more than one value is passed

Value

Object of class rtMod

Author(s)

E.D. Gennatas

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), s_BayesGLM(), s_C50(), s_CTree(), s_EVTree(), s_GAM(), s_GBM(), s_GLM(), s_GLMNET(), s_GLMTree(), s_GLS(), s_H2ODL(), s_H2OGBM(), s_H2ORF(), s_HAL(), s_Isotonic(), s_KNN(), s_LDA(), s_LM(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MARS(), s_MLRF(), s_NBayes(), s_NLA(), s_NLS(), s_NW(), s_PPR(), s_PolyMARS(), s_QDA(), s_QRNN(), s_RF(), s_RFSRC(), s_Ranger(), s_SDA(), s_SGD(), s_SPLS(), s_SVM(), s_TFN(), s_XGBoost(), s_XRF()

Other Tree-based methods: s_AdaBoost(), s_AddTree(), s_BART(), s_C50(), s_CTree(), s_EVTree(), s_GBM(), s_GLMTree(), s_H2OGBM(), s_H2ORF(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MLRF(), s_RF(), s_RFSRC(), s_Ranger(), s_XGBoost(), s_XRF()