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rmlnomogram: Construct Explainable Nomogram for a Machine Learning Model (original) (raw)

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 SufriyanaORCID iD [aut, cre], Emily Chia-Yu Su ORCID iD [aut]
Maintainer: Herdiantri Sufriyana
License: MIT + file
NeedsCompilation: no
Materials: README
CRAN checks: rmlnomogram results

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