Run.Training: The workhorse function for the training workflow
In crarlus/paprbag: Pathogenicity Prediction of Bacterial Genomes

Description Usage Arguments Details Value Author(s) See Also Examples

The workhorse function for the training workflow

Run.Training(Path2FeatureFile, Path2LabelFile = NULL,
  SelectedFeatures = NULL, savePath = NULL, ReturnForest = F,
  verbose = T, min.node.size = 1, num.trees = 100, mtry = NULL,
  importance = "impurity", num.threads = NULL, ...)

`Path2FeatureFile`	Path to feature file in rds format. Rows are independent data, columns are features. May also contain the label information (no default)
`Path2LabelFile`	Either a path to a file containing the label data or (default: NULL)
`SelectedFeatures`	Either a vector of column numbers, or a vector of feature group names (default: NULL)
`savePath`	Path to where the forest object "randomForest.rds" is written to. If NULL, no output is written(default: NULL)
`ReturnForest`	Whether the forest object should be returned or not (default: F)
`verbose`	(default: T)
`min.node.size`	Option for ranger: Minimal node size. (default: 1)
`num.trees`	Option for ranger: Number of trees. (default: 100)
`mtry`	Option for ranger: Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables.
`importance`	Option for ranger: Variable importance mode, one of 'none', 'impurity', 'permutation'. (default: 'impurity')
`num.threads`	Option for ranger: Number of threads. Default is number of CPUs available.
`...`	Additional parameters passed to function ranger, see `ranger::ranger`

Trains a random forest classification algorithm on the Feature file in Trainingfolder using the labels from LabelFile or LabelType. Saves the output (the random forest object) in a new subfolder of TrainingData

Returns either TRUE if forest has been saved successfully or returns the forest object itself

Carlus Deneke

Other TrainingFunctions: Create.TrainingDataSet, SelectFeatureSubset

## Not run: 
Run.Training (Path2FeatureFile = Path2FeatureFile, Path2LabelFile = Path2LabelFile, SelectedFeatures = NULL, savePath = savePath, ReturnForest = F, verbose = T, min.node.size = 1, num.trees = 100, mtry = NULL, importance = 'impurity', num.threads = NULL)

## End(Not run)