Transformation of Features with Package mlfeaturer
2025-05-25
Introduction
Package mlfeaturer aims to provide data manipulation support for a subset of machine learning problems, currently regression problems with multiple input variables and a single input variable and a single input variable . A typical initial task is to transform and scale the variables, split them into training and test sets, and then and, after model fitting, compare the model outputs with the original data, with the test data, and with test data, and with independent new data.
It fills a gap between a style of creating a large number of intermediate data frames and variables, as found in some introductory tutorials, and full-fledged packages like caret, recipes or mlr3.
It uses S4 object orientation, is intentionally simple and mostly developed for our own tasks.
Installation
remotes::install_.packages_github("https://github.com/tpetzoldt/mlfeaturer")Example
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Acknowledgments
Many thanks to the R Core Team (R Core Team 2024) for developing and maintaining R. This documentation was written using knitr (Xie 2024) and rmarkdown (Allaire et al. 2024).