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A model fitted on the training samples, can be validated on a separate validating set. The recall, precision, and accuracy of the model are computed.
doctor.validate(true.labels, predictions)
A vector of 0 and 1.
F-measure, precision, and recall are calculated. Also, the mis-labeled cases are reported.
F-measure is equal to: 2 times precision times recall / (precision+recall).
"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
FeaLect
train.doctor
doctor.validate
random.subset
compute.balanced
compute.logistic.score
ignore.redundant
input.check.FeaLect
# NOT RUN { tls <- c(1,1,1,0,0) ps <- c(1,1,0,1,0) names(tls) <- 1:5; names(ps) <- 1:5 doctor.validate(true.labels=tls, predictions=ps) # }
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