Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of methodologies such as data splitting, cross-validation methods, best model criteria and Y-randomization. RRegrs is a new R package, available at https://www.github.com/enanomapper/RRegrs (0.05 release), which suggests an integrated framework to assist model selection and speed up the process of predictive model development. The tool proposes a fully validated scheme by employing repeated 10-fold and leave-one-out cross-validation for ten linear and non-linear regression methods. Standardized reports are produced to compare the output of modelling algorithms and assess cross-validation results for selected models. Here, we demonstrate RRegrs capabilities in terms of performance using five well-established data sets.
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Using the RRegrs R package for automating predictive modelling
Published: 04 December 2015 by MDPI in MOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed. congress CHEMBIO.INFO-01: Cheminfo., Chemom., Comput. Quantum Chem. & Bioinfo. Congress, Cambridge, UK-Chapel Hill and Richmond, USA, 2015
Keywords: Multiple regression; QSAR; cross-validation; model selection