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undergraduate thesis
Učinkovitost primjene metode analize glavnih komponenti u izgradnji modela predviđanja
2021. urn:nbn:hr:190:270526
Vargić, Anamaria
University of Rijeka
Faculty of Engineering
Department of Computer Engineering
Section of Software Engineering

Cite this document

Vargić, A. (2021). Učinkovitost primjene metode analize glavnih komponenti u izgradnji modela predviđanja (Undergraduate thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:270526

Vargić, Anamaria. "Učinkovitost primjene metode analize glavnih komponenti u izgradnji modela predviđanja." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2021. https://urn.nsk.hr/urn:nbn:hr:190:270526

Vargić, Anamaria. "Učinkovitost primjene metode analize glavnih komponenti u izgradnji modela predviđanja." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2021. https://urn.nsk.hr/urn:nbn:hr:190:270526

Vargić, A. (2021). 'Učinkovitost primjene metode analize glavnih komponenti u izgradnji modela predviđanja', Undergraduate thesis, University of Rijeka, Faculty of Engineering, accessed 07 May 2024, https://urn.nsk.hr/urn:nbn:hr:190:270526

Vargić A. Učinkovitost primjene metode analize glavnih komponenti u izgradnji modela predviđanja [Undergraduate thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2021 [cited 2024 May 07] Available at: https://urn.nsk.hr/urn:nbn:hr:190:270526

A. Vargić, "Učinkovitost primjene metode analize glavnih komponenti u izgradnji modela predviđanja", Undergraduate thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:190:270526

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Metadata
TitleUčinkovitost primjene metode analize glavnih komponenti u izgradnji modela predviđanja
Title (english)Efficiency of principal componenet analysis in predictive modelling
AuthorAnamaria Vargić
MentorGoran Mauša (mentor)
Committee memberKristijan Lenac (predsjednik povjerenstva)
Committee memberGoran Mauša (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Computer Engineering)
(Section of Software Engineering)
Rijeka
Defense date and country2021-09-14, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Na četiri različita skupa podataka su trenirani algoritmi strojnog učenja; stablo odluke i nasumična šuma. Promatrane su metrike za evaluaciju modela i utrošak memorije, vremena i korištenje procesora za slučajeve kada je nad skupom podataka primijenjena tehnika analize glavnih komponenti i kada nije. Zaključeno je da kvaliteta modela uvelike ovisi o količini atributa, uzoraka, vrijednostima podataka te broju mogućih klasa. Smanjenje dimenzionalnosti skupa podataka može značajno smanjiti utrošak memorije i vremena, te kod manjih skupova podataka se može smanjiti točnost modela.
Abstract (english)
Machine learning algorithms decision tree and random forest, are trained on four different datasets. Parameters observed are scores for evaluating machine learning models, time, memory and processor usage for two separate cases. The first case when models are trained on datasets transformed by using principal component analysis and the second case where models are trained on the original datasets. The conclusion is that the quality of the model is highly dependent upon the number of attributes and rows, the values of the data and the amount of possible classes. Dimensionality reduction can greatly reduce time and memory consumption, but for smaller datasets this comes at the cost of model accuracy reduction.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:270526
Study programmeTitle: Computer Engineering
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
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2021-10-13 07:33:36