s_SDA: Sparse Linear Discriminant Analysis

In egenn/rtemis: Machine Learning and Visualization

Sparse Linear Discriminant Analysis

Description

Train an SDA Classifier using sparseLDA::sda

Usage

s_SDA(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  lambda = 1e-06,
  stop = NULL,
  maxIte = 100,
  Q = NULL,
  tol = 1e-06,
  .preprocess = setup.preprocess(scale = TRUE, center = TRUE),
  upsample = TRUE,
  downsample = FALSE,
  resample.seed = NULL,
  x.name = NULL,
  y.name = NULL,
  grid.resample.params = setup.resample("kfold", 5),
  gridsearch.type = c("exhaustive", "randomized"),
  gridsearch.randomized.p = 0.1,
  metric = NULL,
  maximize = NULL,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  grid.verbose = verbose,
  trace = 0,
  outdir = NULL,
  n.cores = rtCores,
  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
lambda	L2-norm weight for elastic net regression
stop	If STOP is negative, its absolute value corresponds to the desired number of variables. If STOP is positive, it corresponds to an upper bound on the L1-norm of the b coefficients. There is a one to one correspondence between stop and t. The default is -p (-the number of variables).
maxIte	Integer: Maximum number of iterations
Q	Integer: Number of components
tol	Numeric: Tolerance for change in RSS, which is the stopping criterion
.preprocess	List of preprocessing parameters. Scaling and centering is enabled by default, because it is crucial for algorithm to learn.
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)
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.
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.
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.
grid.verbose	Logical: Passed to gridSearchLearn
trace	Integer: passed to sparseLDA::sda
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
n.cores	Integer: Number of cores to use.
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)

Value

rtMod object

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_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_SGD(), s_SPLS(), s_SVM(), s_TFN(), s_XGBoost(), s_XRF()

Examples

## Not run: 
datc2 <- iris[51:150, ]
datc2$Species <- factor(datc2$Species)
resc2 <- resample(datc2)
datc2_train <- datc2[resc2$Subsample_1, ]
datc2_test <- datc2[-resc2$Subsample_1, ]
# Without scaling or centering, fails to learn
mod_c2 <- s_SDA(datc2_train, datc2_test, .preprocess = NULL)
# Learns fine with default settings (scaling & centering)
mod_c2 <- s_SDA(datc2_train, datc2_test)

## End(Not run)

egenn/rtemis documentation built on Nov. 22, 2024, 4:12 a.m.