cvms
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).
- Version1.6.2
- R version≥ 3.5
- LicenseMIT
- Licensefile LICENSE
- Needs compilation?No
- Last release07/31/2024
Documentation
- Vignettecreating_confusion_matrix
- Vignettesource
- VignetteR code
- Vignetteavailable_metrics
- Vignettesource
- VignetteR code
- Vignettecross_validating_custom_functions
- Vignettesource
- VignetteR code
- Vignetteevaluate_by_id
- Vignettesource
- VignetteR code
- Vignettepicking_the_number_of_folds_for_cross-validation
- Vignettesource
- VignetteR code
- MaterialREADME
- MaterialNEWS
Team
Ludvig Renbo Olsen
Hugh Benjamin Zachariae
Show author detailsRolesAuthorIndrajeet Patil
Daniel Lüdecke
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends1 package
- Imports18 packages
- Suggests14 packages
- Reverse Imports3 packages
- Reverse Suggests1 package