44 Training Options

Metric options built into the train function:

For continuous outcomes:

  • \(RMSE\)
  • \(R^2\)

For categorical outcomes:

  • Accuracy (Fraction correct)
  • Kappa (Measure of concordance)

44.1 trainControl Resampling

## function (method = "boot", number = ifelse(grepl("cv", method), 
##     10, 25), repeats = ifelse(grepl("[d_]cv$", method), 1, NA), 
##     p = 0.75, search = "grid", initialWindow = NULL, horizon = 1, 
##     fixedWindow = TRUE, skip = 0, verboseIter = FALSE, returnData = TRUE, 
##     returnResamp = "final", savePredictions = FALSE, classProbs = FALSE, 
##     summaryFunction = defaultSummary, selectionFunction = "best", 
##     preProcOptions = list(thresh = 0.95, ICAcomp = 3, k = 5, 
##         freqCut = 95/5, uniqueCut = 10, cutoff = 0.9), sampling = NULL, 
##     index = NULL, indexOut = NULL, indexFinal = NULL, timingSamps = 0, 
##     predictionBounds = rep(FALSE, 2), seeds = NA, adaptive = list(min = 5, 
##         alpha = 0.05, method = "gls", complete = TRUE), trim = FALSE, 
##     allowParallel = TRUE) 
## NULL

Method:

  • boot = bootstrapping
  • boot632 = bootstrapping with adjustment
  • cv = cross validation
  • repeatedcv = repeated cross validation
  • LOOCV = Leave one out cross validation

Number:

  • For boot/cross validation
  • Number of subsamples to take

Repeats:

  • Number of times to repeat subsampling
  • If this number is large computation time will really increase