mikropml-package: mikropml: User-Friendly R Package for Robust Machine Learning...

In SchlossLab/mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines

mikropml: User-Friendly R Package for Robust Machine Learning Pipelines

Description

mikropml implements supervised machine learning pipelines using regression, support vector machines, decision trees, random forest, or gradient-boosted trees. The main functions are preprocess_data() to process your data prior to running machine learning, and run_ml() to run machine learning.

Authors

• Begüm D. Topçuoğlu (ORCID)

• Zena Lapp (ORCID)

• Kelly L. Sovacool (ORCID)

• Evan Snitkin (ORCID)

• Jenna Wiens (ORCID)

• Patrick D. Schloss (ORCID)

See vignettes

• Introduction

• Preprocessing data

• Hyperparameter tuning

• Parallel processing

• The mikropml paper

Author(s)

Maintainer: Kelly Sovacool [e-mail] (ORCID)

Authors:

• Begüm Topçuoğlu [e-mail] (ORCID)

• Zena Lapp [e-mail] (ORCID)

• Evan Snitkin (ORCID)

• Jenna Wiens (ORCID)

• Patrick Schloss [e-mail] (ORCID)

Other contributors:

• Nick Lesniak [e-mail] (ORCID) [contributor]

• Courtney Armour [e-mail] (ORCID) [contributor]

• Sarah Lucas [e-mail] (ORCID) [contributor]

See Also

Useful links:

• https://www.schlosslab.org/mikropml/

• https://github.com/SchlossLab/mikropml

• Report bugs at https://github.com/SchlossLab/mikropml/issues

SchlossLab/mikropml documentation built on Aug. 24, 2023, 9:51 p.m.