s_GLMNET: GLM with Elastic Net Regularization [C, R, S]

In egenn/rtemis: Machine Learning and Visualization

GLM with Elastic Net Regularization [C, R, S]

Description

Train an elastic net model

Usage

s_GLMNET(
  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,
  intercept = TRUE,
  nway.interactions = 0,
  family = NULL,
  alpha = seq(0, 1, 0.2),
  lambda = NULL,
  nlambda = 100,
  which.cv.lambda = c("lambda.1se", "lambda.min"),
  penalty.factor = NULL,
  weights = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  res.summary.fn = mean,
  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
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.
intercept	Logical: If TRUE, include intercept in the model.
nway.interactions	Integer: Number of n-way interactions to include in the model.
family	Error distribution and link function. See stats::family
alpha	[gS] Float [0, 1]: The elasticnet mixing parameter: a = 0 is the ridge penalty, a = 1 is the lasso penalty
lambda	[gS] Float vector: Best left to NULL, cv.glmnet will compute its own lambda sequence
nlambda	Integer: Number of lambda values to compute
which.cv.lambda	Character: Which lambda to use for prediction: "lambda.1se" or "lambda.min"
penalty.factor	Float vector: Multiply the penalty for each coefficient by the values in this vector. This is most useful for specifying different penalties for different groups of variables
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)
res.summary.fn	Function: Used to average resample runs.
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

s_GLMNET runs glmnet::cv.glmnet for each value of alpha, for each resample in grid.resample.params. Mean values for min.lambda and MSE (Regression) or Accuracy (Classification) are aggregated for each alpha and resample combination

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

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_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_SVM(), s_TFN(), s_XGBoost(), s_XRF()

Other Interpretable models: s_AddTree(), s_C50(), s_CART(), s_GLM(), s_GLMTree(), s_LMTree()

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