s_MARS: Multivariate adaptive regression splines (MARS) (C, R)

In egenn/rtemis: Machine Learning and Visualization

Multivariate adaptive regression splines (MARS) (C, R)

Description

Trains a MARS model using earth::earth. [gS] in Arguments description indicates that hyperparameter will be tuned if more than one value are provided For more info on algorithm hyperparameters, see ?earth::earth

Usage

s_MARS(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  x.name = NULL,
  y.name = NULL,
  grid.resample.params = setup.grid.resample(),
  weights = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  glm = NULL,
  degree = 2,
  penalty = 3,
  nk = NULL,
  thresh = 0,
  minspan = 0,
  endspan = 0,
  newvar.penalty = 0,
  fast.k = 2,
  fast.beta = 1,
  linpreds = FALSE,
  pmethod = "forward",
  nprune = NULL,
  nfold = 4,
  ncross = 1,
  stratify = TRUE,
  wp = NULL,
  na.action = na.fail,
  metric = NULL,
  maximize = NULL,
  n.cores = rtCores,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  trace = 0,
  save.mod = FALSE,
  outdir = NULL,
  ...
)

Arguments

x	Numeric vector or matrix of features, i.e. independent variables
y	Numeric vector of outcome, i.e. dependent variable
x.test	(Optional) Numeric vector or matrix of validation set features must have set of columns as x
y.test	(Optional) Numeric vector of validation set outcomes
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.
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)
glm	List of parameters to pass to glm
degree	[gS] Integer: Maximum degree of interaction. Default = 2
penalty	[gS] Float: GCV penalty per knot. 0 penalizes only terms, not knots. -1 means no penalty. Default = 3
nk	[gS] Integer: Maximum number of terms created by the forward pass. See earth::earth
thresh	[gS] Numeric: Forward stepping threshold. Forward pass terminates if RSq reduction is less than this.
pmethod	[gS] Character: Pruning method: "backward", "none", "exhaustive", "forward", "seqrep", "cv". Default = "forward"
nprune	[gS] Integer: Max N of terms (incl. intercept) in the pruned model
na.action	How to handle missing values. See ?na.fail
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.
trace	Integer: If higher than 0, will print more information to the console.
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)
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
...	Additional parameters to pass to earth::earth

Value

Object of class rtMod

Author(s)

E.D. Gennatas

See Also

train_cv for external cross-validation

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_HAL(), s_KNN(), s_LDA(), s_LM(), s_LMTree(), s_LightCART(), s_LightGBM(), 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()

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