splitDataare sampled using default sample functions.
Create the settings for defining how the trainData from
splitData are sampled using
default sample functions.
createSampleSettings( type = "none", numberOutcomestoNonOutcomes = 1, sampleSeed = sample(10000, 1) )
(character) Choice of:
'none' No sampling is applied - this is the default
'underSample')Undersample the non-outcome class to make the data more ballanced
'overSample'Oversample the outcome class by adding in each outcome multiple times
(numeric) An numeric specifying the require number of non-outcomes per outcome
(numeric) A seed to use when splitting the data for reproducibility (if not set a random number will be generated)
An object of class
Returns an object of class
sampleSettings that specifies the sampling function that will be called and the settings