Title:	Contains the Trained 'text2sdg' Ensemble Model Data
Version:	0.1.1
Date:	2023-3-17
Description:	This is a companion package for the 'text2sdg' package. It contains the trained ensemble models needed by the 'detect_sdg' function from the 'text2sdg' package. See Wulff, Meier and Mata (2023) <doi:10.48550/arXiv.2301.11353> and Meier, Wulff and Mata (2021) <doi:10.48550/arXiv.2110.05856> for reference.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.1.2
Depends:	R (≥ 2.10)
LazyData:	true
LazyDataCompression:	bzip2
URL:	https://github.com/psychobas/text2sdgData
BugReports:	https://github.com/psychobas/text2sdgData/issues
NeedsCompilation:	no
Packaged:	2023-03-17 11:11:02 UTC; dominik
Author:	Dominik S. Meier [aut, cre]
Maintainer:	Dominik S. Meier <dominikmeier@outlook.com>
Repository:	CRAN
Date/Publication:	2023-03-17 12:10:06 UTC

A list of trained ranger::ranger() random forest models that are used by the text2sdg detect_sdg() function.

Description

Ensemble models based on a random forest architecture that pools the predictions of six labeling systems generated using the detect_sdg_systems() function from the text2sdg package and also considers text length.

Usage

ensembles

Format

An object of class list of length 4.

Source

Wulff, D. U., Meier, D., & Mata, R. (2023). Using novel data and ensemble models to improve automated SDG-labeling. arXiv