User-Friendly R Package for Supervised Machine Learning Pipelines

An interface to build machine learning models for classification and regression problems. 'mikropml' implements the ML pipeline described by Topçuoğlu et al. (2020) with reasonable default options for data preprocessing, hyperparameter tuning, cross-validation, testing, model evaluation, and interpretation steps. See the website < http://www.schlosslab.org/mikropml/> for more information, documentation, and examples.


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("mikropml")

0.0.2 by Kelly Sovacool, 2 months ago


http://www.schlosslab.org/mikropml/, https://github.com/SchlossLab/mikropml


Report a bug at https://github.com/SchlossLab/mikropml/issues


Browse source code at https://github.com/cran/mikropml


Authors: Begüm Topçuoğlu [aut] , Zena Lapp [aut] , Kelly Sovacool [aut, cre] , Evan Snitkin [aut] , Jenna Wiens [aut] , Patrick Schloss [aut] , Nick Lesniak [ctb]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports caret, dplyr, e1071, glmnet, kernlab, MLmetrics, randomForest, rlang, rpart, stats, utils, xgboost

Suggests doFuture, foreach, future, future.apply, ggplot2, knitr, purrr, rmarkdown, testthat, tidyr


See at CRAN