Benchmarking machine learning models for the analysis of genetic data using FRESA.CAD Binary Classification Benchmarking
De Velasco Oriol, J.; Martinez-Torteya, A.; Trevino, V.; Alanis, I.; Vallejo, E. E.; Tamez-Pena, J. G.
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BackgroundMachine learning models have proven to be useful tools for the analysis of genetic data. However, with the availability of a wide variety of such methods, model selection has become increasingly difficult, both from the human and computational perspective.\n\nResultsWe present the R package FRESA.CAD Binary Classification Benchmarking that performs systematic comparisons between a collection of representative machine learning methods for solving binary classification problems on genetic datasets.\n\nConclusionsFRESA.CAD Binary Benchmarking demonstrates to be a useful tool over a variety of binary classification problems comprising the analysis of genetic data showing both quantitative and qualitative advantages over similar packages.
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