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master's thesis
Usporedba algoritama temeljenih na paradigmi gradient boostinga: Primjena na stvarne medicinske i fizikalne skupove podataka
Zagreb: University of Zagreb, Faculty of Science, 2024. urn:nbn:hr:217:145560
Brkičić, Alan
University of Zagreb
Faculty of Science
Department of Physics

Institutional repository: Repository of the Faculty of Science

Cite this document

Brkičić, A. (2024). Usporedba algoritama temeljenih na paradigmi gradient boostinga: Primjena na stvarne medicinske i fizikalne skupove podataka (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:145560

Brkičić, Alan. "Usporedba algoritama temeljenih na paradigmi gradient boostinga: Primjena na stvarne medicinske i fizikalne skupove podataka." Master's thesis, University of Zagreb, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:217:145560

Brkičić, Alan. "Usporedba algoritama temeljenih na paradigmi gradient boostinga: Primjena na stvarne medicinske i fizikalne skupove podataka." Master's thesis, University of Zagreb, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:217:145560

Brkičić, A. (2024). 'Usporedba algoritama temeljenih na paradigmi gradient boostinga: Primjena na stvarne medicinske i fizikalne skupove podataka', Master's thesis, University of Zagreb, Faculty of Science, accessed 22 March 2025, https://urn.nsk.hr/urn:nbn:hr:217:145560

Brkičić A. Usporedba algoritama temeljenih na paradigmi gradient boostinga: Primjena na stvarne medicinske i fizikalne skupove podataka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2024 [cited 2025 March 22] Available at: https://urn.nsk.hr/urn:nbn:hr:217:145560

A. Brkičić, "Usporedba algoritama temeljenih na paradigmi gradient boostinga: Primjena na stvarne medicinske i fizikalne skupove podataka", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:217:145560

Metadata
TitleUsporedba algoritama temeljenih na paradigmi gradient boostinga: Primjena na stvarne medicinske i fizikalne skupove podataka
Title (english)Comparison of algorithms based on the gradient boosting paradigm: Application to real medical and physical datasets
AuthorAlan Brkičić
MentorDavor Horvatić (mentor)
Committee memberDavor Horvatić (predsjednik povjerenstva)
Committee memberKatarina Jeličić (član povjerenstva)
Committee memberMaro Cvitan (član povjerenstva)
Committee memberMaja Planinić (član povjerenstva)
Committee memberTomislav Bokulić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Physics)
Zagreb
Defense date and country2024-09-27, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Physics
Abstract
U ovom radu se proučavaju razni pristupi strojnom učenju s naglaskom na algoritme bazirane na paradigmi gradient boostinga, ulazi se u njihovu matematičku pozadinu i u dubine funkcioniranja algoritama. Uspoređuje se efikasnost gradient boostinga, XGBoosta, LightGBM-a, CatBoosta i slučajne šume na dva skupa podataka. Skup podataka Higgsovog bozona nastoji identificirati Higgsov bozon unutar raznih signala. Skup podataka za emboliju nastoji predvidjeti hoće li pacijent preživjeti do svoje... More sljedeće kontrole. Rezultati navedenih algoritama se mjere njihovom točnošću, opozivom, preciznošću i ROC-AUC vrijednosti. Mjeri se i značajnost značajki pripadnih skupova podataka pomoću Shapleyjevih vrijednosti. Less
Abstract (english)
This thesis examines various approaches to machine learning, with a focus on algorithms based on the gradient boosting paradigm. It delves into their mathematical foundations and the intricacies of their inner workings. The efficiency of gradient boosting, XGBoost, LightGBM, CatBoost, and random forests is compared using two datasets: the Higgs boson dataset, which aims to identify the Higgs boson among various signals, and the embolism dataset, which seeks to predict patient outcomes... More based on disease severity and other comorbidities. The performance of these algorithms is evaluated using accuracy, recall, precision, and ROC-AUC values. Additionally, the signficance of the features in each dataset is assessed using Shapley values. Less
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:145560
Study programmeTitle: The Integrated University Undergraduate and Graduate Programme in Physics and Computer Science Education; specializations in: Physics and informatics
Course: Physics and informatics
Study programme type: university
Study level: integrated undergraduate and graduate
Academic / professional title: sveučilišni magistar edukacije fizike i informatike (sveučilišni magistar edukacije fizike i informatike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2025-01-31 12:55:10
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