prikaz prve stranice dokumenta Usporedba uspješnosti različitih algoritama strojnog učenja na različitim skupovima podataka
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undergraduate thesis
Usporedba uspješnosti različitih algoritama strojnog učenja na različitim skupovima podataka
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:080611
Anđelković, Ivan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Anđelković, I. (2020). Usporedba uspješnosti različitih algoritama strojnog učenja na različitim skupovima podataka (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:080611

Anđelković, Ivan. "Usporedba uspješnosti različitih algoritama strojnog učenja na različitim skupovima podataka." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:080611

Anđelković, Ivan. "Usporedba uspješnosti različitih algoritama strojnog učenja na različitim skupovima podataka." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:080611

Anđelković, I. (2020). 'Usporedba uspješnosti različitih algoritama strojnog učenja na različitim skupovima podataka', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 28 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:080611

Anđelković I. Usporedba uspješnosti različitih algoritama strojnog učenja na različitim skupovima podataka [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 August 28] Available at: https://urn.nsk.hr/urn:nbn:hr:168:080611

I. Anđelković, "Usporedba uspješnosti različitih algoritama strojnog učenja na različitim skupovima podataka", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:080611

Metadata
TitleUsporedba uspješnosti različitih algoritama strojnog učenja na različitim skupovima podataka
Title (english)Comparing Efficiency of Various Machine Learning Algorithms on Different Datasets
AuthorIvan Anđelković
MentorMarin Šilić (mentor)
Committee memberMarin Šilić (predsjednik povjerenstva)
Committee memberPetar Afrić (član povjerenstva)
Committee memberLucija Šikić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-07-03, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Algoritmi strojnog učenja znaju biti složeni. U ovom radu prikazan je No Free Lunch teorem koji naglašava nepostojanje optimalnog algoritma strojnog ucenja. Pri- kazani su problemi i pozitivne stvari vezane uz nadzirano strojno ucenje. Objašnjeni su glavni tradicionalni algoritmi klasifikacije. Odabrani su predstavnici tih algoritama te su stavljeni u uporabu na izabranim skupovima podataka. Rezultati uporabe tablično prikazani te kasnije komentirani osnovano na ocekivanjima po algoritmima. Prikazan je jedan način formatiranja podataka za uporabu. Objašnjeni osnovni načini validacije rezultantnih modela. Prikazane mane točnosti i prikazana F1-mjera kao alternativa u slučajevima kada je to prikladno.
Abstract (english)
Machine learning algorithms can be complex. In this work the No Free Lunch theorem is shown which emphasizes the fact that an optimal machine learning algorithm does not exist. The main issues and positives are presented which are mostly tied to supervised machine learning. Main traditional classification algorithms are shown and explained. Some of the algorithms are later chosen and put to work on selected datasets. The results of this activity are shown using tables and later discussed based on the expectations which were in place. This work explains one way in which datasets are formatted for use. Some of the main ways of model validation are explained. The flaws of accuracy as a way of validations are shown and the alternative, F1-score is explained and put to use on datasets where it is appropriate.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:080611
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-14 08:18:05