s_HAL: Highly Adaptive LASSO [C, R, S]
In egenn/rtemis: Machine Learning and Visualization

s_HAL

R Documentation

Highly Adaptive LASSO [C, R, S]

Description

Train a HAL model

Usage

s_HAL(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  family = NULL,
  max_degree = ifelse(ncol(X) >= 20, 2, 3),
  lambda = NULL,
  x.name = NULL,
  y.name = NULL,
  grid.resample.params = setup.resample("kfold", 5),
  gridsearch.type = c("exhaustive", "randomized"),
  gridsearch.randomized.p = 0.1,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  metric = NULL,
  maximize = NULL,
  .gs = FALSE,
  n.cores = rtCores,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  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
`family`	Error distribution and link function. See `stats::family`
`lambda`	Float vector: hal9001::fit_hal lambda
`x.name`	Character: Name for feature set
`y.name`	Character: Name for outcome
`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.
`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)
`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.
`.gs`	Internal use only
`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.
`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

Details

⁠\[gS\]⁠ Indicates tunable hyperparameters: If more than a single value is provided, grid search will be automatically performed

Author(s)

E.D. Gennatas

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), s_BayesGLM(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GAM(), s_GAM.default(), s_GAM.formula(), s_GBM(), s_GLM(), s_GLMNET(), s_GLMTree(), s_GLS(), s_H2ODL(), s_H2OGBM(), s_H2ORF(), 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()