Vzporedni homogeni ansambli napovednih modelov

naključni gozd

Rok Lindič (Author), Ljupčo Todorovski (Mentor)

Abstract

V strojnem učenju uporabljamo ansamble napovednih modelov, da zmanjšamo napako, ki bi se pojavila ob uporabi samo enega modela. Pogosto uporabljena ansambelska metoda je naključni gozd. V diplomskem delu bomo opisali, kako naključni gozd deluje, kaj so njegove prednosti v primerjavi z odločitvenimi drevesi, na koncu pa bomo preučili vpliv raznolikosti osnovnih modelov v ansamblih na njihovo napovedno napako.

Keywords

strojno učenje;odločitvena drevesa;ansambli;homogeni ansambli;naključni gozdovi;dekompozicija napovedne napake;

Data

Language:	Slovenian
Year of publishing:	2025
Typology:	2.11 - Undergraduate Thesis
Organization:	UL FMF - Faculty of Mathematics and Physics
Publisher:	[R. Lindič]
UDC:	004.8
COBISS:	242139907
Views:	102
Downloads:	33
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Homogenous parallel ensemble methods: random forest
Secondary abstract:	In machine learning, ensembles of prediction models are used in order to reduce the error that would occur if only one model was used. A commonly used ensemble method is the random forest. The thesis will describe how the random forest functions and what its advantages are compared to decision trees. The end of the thesis will focus on the impact that the diversity of the underlying models in the ensembles has on their prediction error.
Secondary keywords:	machine learning;decision trees;ensembles;homogenous ensembles;random forest;bias-variance decomposition;
Type (COBISS):	Final seminar paper
Study programme:	0
Thesis comment:	Univ. v Ljubljani, Fak. za matematiko in fiziko, Oddelek za matematiko, Finančna matematika - 1. stopnja
Pages:	29 str.
ID:	26796063