fastml: Fast Machine Learning Model Training and Evaluation (original) (raw)

Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code by providing comprehensive data preprocessing and support for a wide range of algorithms with hyperparameter tuning. It offers performance metrics and visualization tools to facilitate efficient and effective machine learning workflows.

Version:	0.7.0
Imports:	methods, recipes, dplyr, ggplot2, reshape2, rsample, parsnip, tune, workflows, yardstick, tibble, rlang, dials, RColorBrewer, baguette, bonsai, discrim, doFuture, finetune, future, plsmod, probably, viridisLite, DALEX, magrittr, pROC, janitor, stringr, DT, UpSetR, VIM, broom, dbscan, ggpubr, gridExtra, htmlwidgets, kableExtra, moments, naniar, plotly, scales, skimr, tidyr, tidyselect, purrr, mice, missForest, survival, flexsurv, rstpm2, iml, lime, survRM2, ceterisParibus, xgboost, knitr, rmarkdown
Suggests:	testthat (≥ 3.0.0), C50, ranger, aorsf, censored, crayon, kernlab, klaR, kknn, keras, lightgbm, rstanarm, mixOmics, pdp, patchwork, GGally, glmnet, agua, bslib, h2o, mlbench, tidyverse
Published:	2025-10-29
DOI:	10.32614/CRAN.package.fastml
Author:	Selcuk Korkmaz [aut, cre], Dincer Goksuluk [aut], Eda Karaismailoglu [aut]
Maintainer:	Selcuk Korkmaz
BugReports:	https://github.com/selcukorkmaz/fastml/issues
License:	MIT + file
URL:	https://selcukorkmaz.github.io/fastml-tutorial/,https://github.com/selcukorkmaz/fastml
NeedsCompilation:	no
Materials:	README
CRAN checks:	fastml results

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