The role of balanced training and testing data sets for binary classifiers in bioinformatics.

ABSTRACT: Training and testing of conventional machine learning models on binary classification problems depend on the proportions of the two outcomes in the relevant data sets. This may be especially important in practical terms when real-world applications of the classifier are either highly imbalanced or occur in unknown proportions. Intuitively, it may seem sensible to train machine learning models on data similar to the target data in terms of proportions of the two binary outcomes. However, we show that this is not the case using the example of prediction of deleterious and neutral phenotypes of human missense mutations in human genome data, for which the proportion of the binary outcome is unknown. Our results indicate that using balanced training data (50% neutral and 50% deleterious) result

SUBMITTER: Wei Q 

PROVIDER: S-EPMC3706434 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature