chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models

Autor: Jeremy R. Ash, Jacqueline M. Hughes-Oliver
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Journal of Cheminformatics, Vol 10, Iss 1, Pp 1-20 (2018)
Druh dokumentu: article
ISSN: 1758-2946
DOI: 10.1186/s13321-018-0309-4
Popis: Abstract The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of these models. While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, all of the methods in chemmodlab have broad utility for the machine learning community. chemmodlab contains several assessment utilities, including a plotting function that constructs accumulation curves and a function that computes many performance measures. The most novel feature of chemmodlab is the ease with which statistically significant performance differences for many machine learning models is presented by means of the multiple comparisons similarity plot. Differences are assessed using repeated k-fold cross validation, where blocking increases precision and multiplicity adjustments are applied. chemmodlab is freely available on CRAN at https://cran.r-project.org/web/packages/chemmodlab/index.html.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje