Provides Python-based extensions to enhance data analytics workflows, particularly for tasks involving data preprocessing and predictive modeling. Includes tools for data sampling, transformation, feature selection, balancing strategies (e.g., SMOTE), and model construction. These capabilities leverage Python libraries via the reticulate interface, enabling seamless integration with a broader machine learning ecosystem. Supports instance selection and hybrid workflows that combine R and Python functionalities for flexible and reproducible analytical pipelines. The architecture is inspired by the Experiment Lines approach, which promotes modularity, extensibility, and interoperability across tools. More information on Experiment Lines is available in Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.
Package details |
|
---|---|
Author | Eduardo Ogasawara [aut, ths, cre] (ORCID: <https://orcid.org/0000-0002-0466-0626>), Diego Salles [aut], Janio Lima [aut], Lucas Tavares [aut], Eduardo Bezerra [ctb], CEFET/RJ [cph] |
Maintainer | Eduardo Ogasawara <eogasawara@ieee.org> |
License | MIT + file LICENSE |
Version | 1.2.727 |
URL | https://cefet-rj-dal.github.io/daltoolboxdp/ https://github.com/cefet-rj-dal/daltoolboxdp |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.