Tehnike kombiniranja napovedi pri strojnem učenju ansamblov : delo diplomskega seminarja

Bizjak, Sara (author), Todorovski, Ljupčo (mentor)

Ideja strojnega učenja ansamblov je zgraditi napovedni model z združevanjem večih modelov, kar pripomore k manjšanju napovedne napake. Ena ključnih komponent ansambla je funkcija za kombiniranje napovedi osnovnih modelov. V diplomskem delu obravnavamo dva tipa funkcij za kombiniranje napovedi klasifikacijskih modelov. Prvi je večinsko glasovanje, kjer vsi osnovni modeli enako prispevajo k napovedi ansambla. Drugi pa je uteževanje prispevka osnovnih modelov na osnovi njihove zmogljivosti. Ti dve funkciji kombiniranja implementiramo v programskem jeziku R in ju primerjamo na izbrani podatkovni množici.

• Language: Slovenian
• Keywords: strojno učenje, nadzorovano strojno učenje, klasifikacija, homogeni ansambli, naključni gozd, kombiniranje napovedi, uteževanje na osnovi zmogljivosti
• Work type: Final seminar paper
• Typology: 2.11 - Undergraduate Thesis
• Organization: FMF - Faculty of Mathematics and Physics
• Year: 2020
• PID: 20.500.12556/RUL-120588
• UDC: 004.8
• COBISS.SI-ID: 58738435
• Publication date in RUL: 23.09.2020

BIZJAK, Sara, 2020, Tehnike kombiniranja napovedi pri strojnem učenju ansamblov : delo diplomskega seminarja [online]. Bachelor’s thesis. [Accessed 26 March 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=120588

Secondary language

• Language: English
• Title: Techniques for combining predictions in machine learning ensembles

Abstract

Machine learning of ensembles aims at reducing the predictive error by integrating multiple models into a single one. One of the key components of algorithms for ensemble learning is combining predictions of the base models. In the thesis, we take a closer look at two functions for combining predictions. The first is majority voting, where all the base models contribute equally to the ensemble prediction. The other is performance weighting, where the contribution of a base model to the ensemble prediction is proportional to the model performance. Combination functions are also implemented in R and tested on a selected data set.

• Keywords: machine learning, supervised machine learning, classification, homogeneous ensembles, random forest, combining predictions, performance weighting