s_SVM: Support Vector Machines (C, R)

In egenn/rtemis: Machine Learning and Visualization

Support Vector Machines (C, R)

Description

Train an SVM learner using e1071::svm

Usage

s_SVM(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  x.name = NULL,
  y.name = NULL,
  grid.resample.params = setup.resample("kfold", 5),
  gridsearch.type = c("exhaustive", "randomized"),
  gridsearch.randomized.p = 0.1,
  class.weights = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  kernel = "radial",
  degree = 3,
  gamma = NULL,
  coef0 = 0,
  cost = 1,
  probability = TRUE,
  metric = NULL,
  maximize = NULL,
  plot.fitted = NULL,
  plot.predicted = NULL,
  print.plot = FALSE,
  plot.theme = rtTheme,
  n.cores = rtCores,
  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
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.
class.weights	Float, length = n levels of outcome: Weights for each outcome class.For classification, class.weights takes precedence over ifw, therefore set class.weights = NULL if using ifw.
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)
kernel	Character: "linear", "polynomial", "radial", "sigmoid"
degree	[gS] Integer: Degree for kernel = "polynomial".
gamma	[gS] Float: Parameter used in all kernels except linear
coef0	[gS] Float: Parameter used by kernels polynomial and sigmoid
cost	[gS] Float: Cost of constraints violation; the C constant of the regularization term in the Lagrange formulation.
probability	Logical: If TRUE, model allows probability estimates
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.
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
print.plot	Logical: if TRUE, produce plot using mplot3 Takes precedence over plot.fitted and plot.predicted.
plot.theme	Character: "zero", "dark", "box", "darkbox"
n.cores	Integer: Number of cores to use.
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)
...	Additional arguments to be passed to e1071::svm

Details

[gS] denotes parameters that will be tuned by cross-validation if more than one value is passed. Regarding SVM tuning, the following guide from the LIBSVM authors can be useful: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf They suggest searching for cost = 2 ^ seq(-5, 15, 2) and gamma = 2 ^ seq(-15, 3, 2)

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_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_TFN(), s_XGBoost(), s_XRF()

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