splitBoost
builds a training and validation set by randomly up-sampling
(with replacement) the smaller of two classes. This results in an equal
representation of each class in the training set. For example, given 30 cases and
3 controls, a 2/3 split would place 20 cases and 20 controls in the training set.
Of these 20 controls, only 2 are unique. The test set is not boosted. In this
example, the test set would contain 10 cases and 1 control.
1 | splitBoost(object, percent.include = 67)
|
object |
An |
percent.include |
Specifies the percent of the total number of subjects to include in the training set (i.e., based on the larger group). Subjects from the smaller group are up-sampled to match this number. |
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