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Title:Kako poštena so klasifikacijska odločitvena drevesa?
Authors:ID Kostić, Andrej (Author)
ID Karakatič, Sašo (Mentor) More about this mentor... New window
ID Colakovic, Ivona (Comentor)
Files:.pdf MAG_Kostic_Andrej_2024.pdf (3,47 MB)
MD5: 116569B7D4D8B1A8642082B02DA8B362
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Poštenost klasifikacijskih odločitvenih dreves je na področju strojnega učenja postala kritično vprašanje. Klasifikacijska in regresijska drevesa (CART) so znana po svoji preprostosti in učinkovitosti pri obravnavanju klasifikacijskih in regresijskih nalog. Vendar lahko ti modeli nehote ohranijo ali celo povečajo pristranskost, prisotno v podatkih, kar vodi do nepoštenih odločitev, ki nesorazmerno prizadenejo določene skupine. To magistrsko delo raziskuje poštenost modelov CART z implementacijo metode FairCART, ki vključuje omejitve poštenosti med postopkom oblikovanja dreves. V delu je ocenjena učinkovitost metode FairCART pri zmanjševanju pristranskosti ob hkratnem ohranjanju kakovosti odločitev, kar omogoča vpogled v kompromise med poštenostjo in točnostjo. Implementacija in rezultati eksperimenta kažejo, da lahko metoda FairCART zmerno zmanjša pristranskost in ohrani splošno kakovost odločitvenega drevesa.
Keywords:klasifikacijska in regresijska drevesa, poštenost v strojnem učenju, CART, FairCART
Place of publishing:Maribor
Publisher:[A. Kostić]
Year of publishing:2024
PID:20.500.12556/DKUM-89793 New window
UDC:004.85(043.2)
COBISS.SI-ID:219212291 New window
Publication date in DKUM:19.09.2024
Views:242
Downloads:48
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:12.08.2024

Secondary language

Language:English
Title:How fair are classification decision trees?
Abstract:The fairness of classification decision trees has become a critical concern in the field of machine learning. Classification And Regression Trees (CART) are renowned for their simplicity and effectiveness in handling classification and regression tasks. However, these models can inadvertently perpetuate or even amplify biases present in the data, leading to unfair decisions that disproportionately affect certain groups. This master's thesis explores the fairness of CART models by implementing the FairCART method, which integrates fairness constraints during the tree-building process. The thesis evaluates the effectiveness of FairCART in reducing biases while maintaining decision quality, providing insights into the trade-offs between fairness and accuracy. The implementation and experimental results demonstrate that the FairCART method can modestly reduce bias and maintain overall decision tree quality.
Keywords:classification and regression trees, fairness in machine learning, CART, FairCART


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