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undergraduate thesis
Postupci ansambla stabala odluke
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. urn:nbn:hr:168:644422
Dašić, Darin
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Dašić, D. (2017). Postupci ansambla stabala odluke (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:644422

Dašić, Darin. "Postupci ansambla stabala odluke." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:644422

Dašić, Darin. "Postupci ansambla stabala odluke." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:644422

Dašić, D. (2017). 'Postupci ansambla stabala odluke', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 17 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:644422

Dašić D. Postupci ansambla stabala odluke [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2017 [cited 2024 May 17] Available at: https://urn.nsk.hr/urn:nbn:hr:168:644422

D. Dašić, "Postupci ansambla stabala odluke", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:168:644422

Metadata
TitlePostupci ansambla stabala odluke
Title (english)Decision Tree Ensemble Methods
AuthorDarin Dašić
MentorAlan Jović (mentor)
Committee memberAlan Jović (predsjednik povjerenstva)
Committee memberDavor Kukolja (član povjerenstva)
Committee memberMartin Soldić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2017-09-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ansambli stabala odluke su modeli strojnog učenja zasnovani na višestrukim osnovnim modelima, stablima odluke. Postoje dvije glavne metode za izgradnju ansambla: boosting i bagging. Boosting iterativno gradi stabla tako da nove iteracije ovisi o prošloj iteraciji, dok stabla izgrađena bagging metodom ovise o bootstrap uzorku za učenje. Prilikom glasanja konačnog ansambla, boosting provodi težinsko glasanje dok bagging ne. Jedna implementacija bagging metode su slučajne šume, koje dodatno uvode slučajnost prilikom odabira atributa pri izgradnji stabla. U ovom radu opisana je izgradnja slučajne šume, ostvarena je programska implementacija metode te je provedena eksperimentalna usporedba vrednovanja rezultata između ovog ostvarenja i alata Weka na nekoliko skupova podataka.
Abstract (english)
Ensemble methods are based on combining multiple base models. There are two main methods for building an ensemble: boosting and bagging. Base models are constructed iteratively in boosting, where each iteration depends on the performance of the previous one. In bagging, base models are constructed upon bootstrap sample from the training data. When the ensemble makes a decision in boosting each base model casts a weighted vote, whereas in bagging each vote counts the same. Random forests are modification of the bagging method where each CART tree is additionally randomized by selecting a random subspace of the attributes for the split. In this work, random forest algorithm is discussed, an implementation is provided and the results of the implementation are compared to those of WEKA.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:644422
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 on2019-03-13 19:54:31