Replication Data for: Regularized Feature Selection Landscapes: An Empirical Study of Multimodality

DOI

This dataset contains replication data for the paper Regularized Feature Selection Landscapes: An Empirical Study of Multimodality. It contains accuracy tables of well-known classification datasets from The UCI Machine Learning Repository. These tables comprise the accuracy for all feature subsets, i.e., all column combinations, obtained by a decision tree classifier, with different levels of regularization.

The decision tree classifier is provided by the BetaML.jl package for the Julia programming language, and uses the default parameters: max_depth=0 (no limits) min_gain=0.0, min_records=2, max_features=0 (consider all features), splitting_criterion=BetaML.Utils.gini, (Gini impurity index), and rng = Random.GLOBAL_RNG (no set seed for random number generation).

Julia, 1.9.3

Identifier
DOI https://doi.org/10.18710/DQZKMX
Metadata Access https://dataverse.no/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18710/DQZKMX
Provenance
Creator Sánchez Diaz, Xavier Fernando Cuauhtémoc (ORCID: 0000−0003−2271−439X)
Publisher DataverseNO
Contributor Sánchez Diaz, Xavier Fernando Cuauhtémoc; NTNU – Norwegian University of Science and Technology; Sánchez Díaz, Xavier Fernando Cuauhtémoc
Publication Year 2024
Rights CC0 1.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Contact Sánchez Diaz, Xavier Fernando Cuauhtémoc (NTNU – Norwegian University of Science and Technology)
Representation
Resource Type machine-readable text; Dataset
Format text/plain; text/comma-separated-values; application/x-tar
Size 4861; 13792; 52986727; 13684; 56453; 27709; 926588; 943660; 3837063; 6732309; 6772272; 72325120
Version 1.0
Discipline Other
Spatial Coverage Trondheim, Norway