MLwrap: Machine Learning Modelling for Everyone
A minimal library specifically designed to make the estimation of
Machine Learning (ML) techniques as easy and accessible as possible,
particularly within the framework of the Knowledge Discovery in
Databases (KDD) process in data mining. The package provides
essential tools to structure and execute each stage of a predictive
or classification modeling workflow, aligning closely with the
fundamental steps of the KDD methodology, from data selection and
preparation, through model building and tuning, to the
interpretation and evaluation of results using Sensitivity Analysis.
The 'MLwrap' workflow is organized into four core steps;
preprocessing(), build_model(), fine_tuning(), and
sensitivity_analysis(). These steps correspond, respectively, to
data preparation and transformation, model construction,
hyperparameter optimization, and sensitivity analysis. The user can
access comprehensive model evaluation results including fit
assessment metrics, plots, predictions, and performance diagnostics
for ML models implemented through 'Neural Networks', 'Random Forest',
'XGBoost' (Extreme Gradient Boosting), and 'Support Vector Machines'
(SVM) algorithms. By streamlining these phases, 'MLwrap' aims to
simplify the implementation of ML techniques, allowing analysts and
data scientists to focus on extracting actionable insights and
meaningful patterns from large datasets, in line with the objectives
of the KDD process.
| Version: |
0.2.1 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
R6, tidyr, magrittr, dials, parsnip, recipes, rsample, tune, workflows, yardstick, vip, glue, innsight, fastshap, DiagrammeR, ggbeeswarm, ggplot2, sensitivity, dplyr, rlang, tibble, patchwork, cli, scales |
| Suggests: |
testthat (≥ 3.0.0), torch, brulee, ranger, kernlab, xgboost |
| Published: |
2025-10-21 |
| DOI: |
10.32614/CRAN.package.MLwrap |
| Author: |
Javier Martínez García
 [aut],
Juan José Montaño Moreno
[ctb],
Albert Sesé [cre,
ctb] |
| Maintainer: |
Albert Sesé <albert.sese at uib.es> |
| BugReports: |
https://github.com/AlbertSesePsy/MLwrap/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/AlbertSesePsy/MLwrap |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
MLwrap results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=MLwrap
to link to this page.