s_C50: C5.0 Decision Trees and Rule-Based Models C
In egenn/rtemis: Machine Learning and Visualization

s_C50

R Documentation

C5.0 Decision Trees and Rule-Based Models C

Description

Train a C5.0 decision tree using C50::C5.0

Usage

s_C50(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  trials = 10,
  rules = FALSE,
  weights = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  control = C50::C5.0Control(),
  costs = NULL,
  x.name = NULL,
  y.name = NULL,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  trace = 0,
  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
`trials`	Integer [1, 100]: Number of boosting iterations
`rules`	Logical: If `TRUE`, decompose the tree to a rule-based model
`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)
`control`	List: output of `C50::C5.0Control()`
`costs`	Matrix: Cost matrix. See `C50::C5.0`
`x.name`	Character: Name for feature set
`y.name`	Character: Name for outcome
`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.
`trace`	Integer: If higher than 0, will print more information to the console.
`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 arguments

Value

rtMod object

Author(s)

E.D. Gennatas

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), s_BayesGLM(), s_CART(), 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_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_CART(), 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()