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undergraduate thesis
Klasifikator pojačanja gradijenta kod procjene mogućnosti preživljavanja putnika na Titaniku
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. urn:nbn:hr:235:573552
Lovrić, Irena
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

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Lovrić, I. (2022). Klasifikator pojačanja gradijenta kod procjene mogućnosti preživljavanja putnika na Titaniku (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:573552

Lovrić, Irena. "Klasifikator pojačanja gradijenta kod procjene mogućnosti preživljavanja putnika na Titaniku." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:573552

Lovrić, Irena. "Klasifikator pojačanja gradijenta kod procjene mogućnosti preživljavanja putnika na Titaniku." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:573552

Lovrić, I. (2022). 'Klasifikator pojačanja gradijenta kod procjene mogućnosti preživljavanja putnika na Titaniku', Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 18 December 2024, https://urn.nsk.hr/urn:nbn:hr:235:573552

Lovrić I. Klasifikator pojačanja gradijenta kod procjene mogućnosti preživljavanja putnika na Titaniku [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2022 [cited 2024 December 18] Available at: https://urn.nsk.hr/urn:nbn:hr:235:573552

I. Lovrić, "Klasifikator pojačanja gradijenta kod procjene mogućnosti preživljavanja putnika na Titaniku", Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:235:573552

Metadata
TitleKlasifikator pojačanja gradijenta kod procjene mogućnosti preživljavanja putnika na Titaniku
Title (english)Gradient boost classifier in the assessment of the survival chance of passengers on Titanic
AuthorIrena Lovrić
MentorTomislav Stipančić (mentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2022-03-02, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
U ovom radu je ukratko objašnjen algoritam Gradient Boost i prikazana matematika na kojoj je baziran i koju je objasnio Jerome H. Freidman. Gradient Boost je pokazao velik uspjeh u širokom spektru primjene u stvarnom svijetu. Također su definirani pojmovi funkcija gubitka (loss function), learning rate i overfitting. Funkcija gubitka i learning rate kao jedne od najznačajnijih faktora u Gradient Boost, te overfitting kao jedan od najvećih problema Gradient Boosta. Također je objašnjen primjer klasifikacije baze podatka koristeći Gradient Boost „Titanic- Top 1% with Gradient Boost Clasifier“. Ovim primjerom se pokušava odrediti koji putnik će preživjeti i koji neće preživjeti potonuće Titanika. Primjer je uzet sa stranice Kaggle i napisan je u Python-u. Kroz objašnjenje primjera smo prošli kroz danu bazu podataka, prilagodili varijable (skupine podatka) našim potrebama, odredili suodnose varijabli, kako je odabran Gradinet Boost i prilagodili hiperparametre.
Abstract (english)
In this pape Gradient Boost Algoritham is briefly explained and math by Jerome H. Freidman on witch it is based. Gradient Boost has shown great success in wide rage of applications. The concepts of loss function, learning rate and overfitting are also defined. The function of loss and learning rate are one of the most important factors in Gradient Boost, and overfitting is one of the biggest problems of Gradient Boost. There is also an explanation of an database classification using Gradiet Boost „Titanic- Top 1% with Gradient Boost Clasifier“. Attempt of this exaple is to determined wich passenger will survive and wich will not survive the sinking of the Titanic.This example was taken from th Kaggle page and it was writen in Python. Going through exaplme , we went over given datebase, adjusted the variables to our needs, determined the correlations of the varibale, how was Gradient Boost selesceted and adjusted the hyperparamethrs.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:573552
Study programmeTitle: Mechanical Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2022-02-24 09:37:43