rmlnomogram: Construct Explainable Nomogram for a Machine Learning Model
Construct an explainable nomogram for a machine learning (ML) model to improve availability of an ML prediction model in addition to a computer application, particularly in a situation where a computer, a mobile phone, an internet connection, or the application accessibility are unreliable. This package enables a nomogram creation for any ML prediction models, which is conventionally limited to only a linear/logistic regression model. This nomogram may indicate the explainability value per feature, e.g., the Shapley additive explanation value, for each individual. However, this package only allows a nomogram creation for a model using categorical without or with single numerical predictors. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rmlnomogram/blob/master/doc/ml_nomogram_exemplar.html>.
| Version: | 0.1.2 |
| Depends: | R (≥ 4.4) |
| Imports: | dplyr,purrr,broom, stats,ggplot2,ggpubr,stringr,tidyr, utils |
| Suggests: | tidyverse,knitr,caret,randomForest,iml,testthat (≥3.0.0) |
| Published: | 2025-01-08 |
| DOI: | 10.32614/CRAN.package.rmlnomogram |
| Author: | Herdiantri Sufriyana [aut, cre], Emily Chia-Yu Su [aut] |
| Maintainer: | Herdiantri Sufriyana <herdi at nycu.edu.tw> |
| License: | MIT + fileLICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | rmlnomogram results |
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