Package: SWORD 0.1.0

SWORD: Support Vector Machine Weighted Oblique Random Decision Forests

Implements SWORD (Support vector machine Weighted Oblique Random Decision forests), a random forest method for regression based on TORS (Tree Oblique for Regression with weighted SVM) oblique regression trees. At each internal node a Weighted-SVM classifier with a linear kernel defines the oblique splitting hyperplane as a sparse linear combination of predictors. The C++ coordinate-descent solver provides fast training. Variable importance is computed via OIW-VI (Oblique Impurity-Weighted Variable Importance). Provides tree fitting (TORS), forest fitting (SWORD), prediction, and variable importance (VI_SWORD). Parallel tree fitting is supported via the 'future'/'furrr' framework. The SWORD method is described in Carta and Frigau (accepted, in press) 'Oblique Random Forests for Regression via Weighted Support Vector Machine', Advances in Data Analysis and Classification. The TORS algorithm is described in Carta and Frigau (2025) <doi:10.1007/s00180-025-01647-w>. The weighted SVM is based on Yang, Song and Cao (2005) <doi:10.1109/IJCNN.2005.1555965> and the 'WeightSVM' package by Xu et al. (2024) <doi:10.32614/CRAN.package.WeightSVM>.

Authors:Andrea Carta [aut, cre], Luca Frigau [aut]