predict.ssenet: iterxM-fold cross-validation function for Semi-supervised...

In singha53/ssenet: Semi-supervised implementation of glmnet

Description

Estimate test error of elastic net panel

Usage

## S3 method for class 'ssenet'
predict(
  object,
  validation = c("Mfold", "loo"),
  M = 5,
  iter = 10,
  ncores = 4,
  progressBar = TRUE
)

Arguments

object	an object of a specific machine learning method (ML) class
validation	a string type of validation; either "Mfold" or "loo"
M	an integer specifying the number of folds
iter	an integer specifying the number of times to repeat the cross-validation
ncores	an object of a specific machine learning method (ML) class
progressBar	a boolean that specifies whether a progress bar should be displayed or not

singha53/ssenet documentation built on March 17, 2020, 4:41 a.m.