s_RuleFit: Rulefit [C, R]
In egenn/rtemis: Machine Learning and Visualization

s_RuleFit

R Documentation

Rulefit [C, R]

Description

Train a gradient boosting model, extract rules, and fit using LASSO

Usage

s_RuleFit(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  gbm.params = list(list(n.trees = 300, bag.fraction = 1, shrinkage = 0.1,
    interaction.depth = 3, ifw = TRUE)),
  meta.alpha = 1,
  meta.lambda = NULL,
  meta.extra.params = list(ifw = TRUE),
  cases.by.rules = NULL,
  x.name = NULL,
  y.name = NULL,
  n.cores = rtCores,
  question = NULL,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  outdir = NULL,
  save.mod = if (!is.null(outdir)) TRUE else FALSE,
  verbose = TRUE
)

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
`gbm.params`	List of named lists: A list, each element of which is a named list of parameters for s_GBM. i.e. If you want to train a single GBM model, this could be: `gbm.params = list( list( n.trees = 300, bag.fraction = 1, shrinkage = .1, interaction.depth = 3, ifw = TRUE ) )` if you wanted to train 2 GBM models, this could be: `gbm.params = list( list( n.trees = 300, bag.fraction = 1, shrinkage = .1, interaction.depth = 3, ifw = TRUE ), list( n.trees = 500, bag.fraction = 1, shrinkage = .1, interaction.depth = 3, ifw = TRUE ) )`
`meta.alpha`	Float [0, 1]: `alpha` for s_GLMNET
`meta.lambda`	Float: `lambda` for s_GLMNET. Default = NULL (will be determined automatically by crossvalidation)
`meta.extra.params`	Named list: Parameters for s_GLMNET for the feature selection step
`cases.by.rules`	Matrix of cases by rules from a previoue rulefit run. If provided, the GBM step is skipped. Default = NULL
`x.name`	Character: Name for feature set
`y.name`	Character: Name for outcome
`n.cores`	Integer: Number of cores to use
`question`	Character: the question you are attempting to answer with this model, in plain language.
`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"
`outdir`	Character: If defined, save log, 'plot.all' plots (see above) and RDS file of complete output
`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)`
`verbose`	Logical: If TRUE, print summary to screen.