Cross-Validation for Model Selection

Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).


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install.packages("cvms")

0.3.1 by Ludvig Renbo Olsen, 2 months ago


https://github.com/ludvigolsen/cvms


Report a bug at https://github.com/ludvigolsen/cvms/issues


Browse source code at https://github.com/cran/cvms


Authors: Ludvig Renbo Olsen [aut, cre] , Benjamin Hugh Zachariae [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports data.table, dplyr, plyr, tidyr, ggplot2, purrr, tibble, caret, pROC, stats, lme4, MuMIn, broom, stringr, mltools, rlang, utils, lifecycle

Suggests knitr, groupdata2, e1071, rmarkdown, testthat, AUC, furrr, ModelMetrics, covr, nnet, randomForest


See at CRAN