sparseCV: cross-validation functions. For internal package use only.
sparseCV(
data,
tune.grid,
hoso = "hoso",
method = "L0",
nfolds = "K",
juliaFnPath = NA,
messageInd = FALSE,
LSitr = 50,
LSspc = 1,
maxIter = 2500
)A list (S3 class) with elements used for cross validation.
A dataframe with the hyperparameters associated with the best prediction performance and summary statistics of performance.
A dataframe including optimal hyperparameters according to 1-standard deviation rule.
A dataframe with prediction performance for hyperparamters in tuning grid for all folds.
A dataframe with average performance at each of the hyperparameters in tuning grid (averaged across tasks).
Matrix with outcome and design matrix
A data.frame of tuning values
String specifying tuning type
Sting specifying regression method
String or integer specifying number of folds
String specifying path to Julia binary
Boolean for message printing
Integer specifying do <LSitr> local search iterations on parameter values where we do actually do LS; NA does no local search
Integer specifying number of hyperparameters to conduct local search: conduct local search every <LSspc>^th iteration. NA does no local search
Integer specifying max iterations of coordinate descent