If negative samples are less than positive ones, more copies of the negative cases are added and vice versa.
compute.balanced(F_, L_)The feature matrix, each column is a feature.
The vector of labels named according to the rows of F.
Returns a list of:
The feature matrix, each column is a feature.
The vector of labels named according to the rows of F.
Considerably unbalanced classes may be probabilistic for fitting some models.
"Statistical Analysis of Overfitting Features", manuscript in preparation.
FeaLect, train.doctor, doctor.validate,
random.subset, compute.balanced,compute.logistic.score,
ignore.redundant, input.check.FeaLect
# NOT RUN {
library(FeaLect)
data(mcl_sll)
F <- as.matrix(mcl_sll[ ,-1]) # The Feature matrix
L <- as.numeric(mcl_sll[ ,1]) # The labels
names(L) <- rownames(F)
message(L)
balanced <- compute.balanced(F_=F, L_=L)
message(balanced$L_)
# }
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