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).


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("cvms")

1.0.2 by Ludvig Renbo Olsen, 4 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 broom, checkmate, data.table, dplyr, ggplot2, lifecycle, lme4, MuMIn, plyr, pROC, purrr, recipes, rlang, stats, stringr, tibble, tidyr, utils

Suggests AUC, covr, e1071, furrr, ggimage, groupdata2, knitr, nnet, randomForest, rmarkdown, rsvg, testthat, xpectr


See at CRAN