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undergraduate thesis
Usporedba metoda strojnog učenja s jasnim i nejasnim tumačenjem modela
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:134938
Pantina, Ardian
University of Zagreb
Faculty of Electrical Engineering and Computing

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Pantina, A. (2019). Usporedba metoda strojnog učenja s jasnim i nejasnim tumačenjem modela (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:134938

Pantina, Ardian. "Usporedba metoda strojnog učenja s jasnim i nejasnim tumačenjem modela." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:134938

Pantina, Ardian. "Usporedba metoda strojnog učenja s jasnim i nejasnim tumačenjem modela." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:134938

Pantina, A. (2019). 'Usporedba metoda strojnog učenja s jasnim i nejasnim tumačenjem modela', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:134938

Pantina A. Usporedba metoda strojnog učenja s jasnim i nejasnim tumačenjem modela [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 May 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:134938

A. Pantina, "Usporedba metoda strojnog učenja s jasnim i nejasnim tumačenjem modela", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:134938

Metadata
TitleUsporedba metoda strojnog učenja s jasnim i nejasnim tumačenjem modela
Title (english)Comparison of Machine Learning Methods with Clear and Unclear Model Interpretation
AuthorArdian Pantina
MentorAlan Jović (mentor)
Committee memberAlan Jović (predsjednik povjerenstva)
Committee memberNikolina Frid (član povjerenstva)
Committee memberIgor Stančin (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Različiti algoritmi klasifikacije, osim po samom pristupu problemu i performansama, razlikuju se i po razini razumljivosti i jednostavnosti objašnjenja čovjeku. Oni se mogu formalno usporediti po preciznosti kvalifikacije nad skupovima podataka koristeći metode poput unakrsne validacije, dok za interpretabilnost formalna definicija ne postoji. Ipak, spo- sobnost shvaćanja kako algoritmi donose odluke je sve bitniji s porastom odgovornosti sus- tava koji te algoritme koriste. Iako je generalan konsenzus da kompleksniji i teže razumljivi algoritmi donose bolje rezultate, oni jednostavniji imaju svoje prednosti i primjenjivi su na odred̄ene probleme.
Abstract (english)
Different classification algorithms differ by their problem approach and performances, but also by their level of interpretability and ease of explanation. They can formally be compared using various methods such as cross validation, while no definition for interpreta- bility exists. However, the ability to clearly explain how an algorithm works is growing in importance with the rapid growth of the algorithms’ responsibilities. Although the general concensus is that more complex algorithms perform better, there are cases where the simpler models can prove more capable.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:134938
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 on2020-01-07 20:55:00