Accuracy Comparison of Logistic Regression, Random Forest, and Neural Networks Applied to Real MaaS Data
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F24%3A00375692" target="_blank" >RIV/68407700:21260/24:00375692 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1109/SCSP61506.2024.10552715" target="_blank" >https://doi.org/10.1109/SCSP61506.2024.10552715</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SCSP61506.2024.10552715" target="_blank" >10.1109/SCSP61506.2024.10552715</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Accuracy Comparison of Logistic Regression, Random Forest, and Neural Networks Applied to Real MaaS Data
Popis výsledku v původním jazyce
The paper deals with a comparative analysis of three widely used data analysis methods: logistic regression, random forest, and neural networks. These methods have been evaluated in terms of accuracy, and computational efficiency and applied to different types of data sets, including both simulated and real MaaS data. The study aims to compare the efficiency of each method in classification tasks. The study leads to specific recommendations on which method to use under various circumstances, contributing to the decision-making process in data analysis projects. We have shown that random forests generally provide better accuracy and are resistant to over-training. Neural networks can achieve comparable performance under certain conditions, although at a high computational cost. Logistic regression shows limitations in dealing with complex data structures.
Název v anglickém jazyce
Accuracy Comparison of Logistic Regression, Random Forest, and Neural Networks Applied to Real MaaS Data
Popis výsledku anglicky
The paper deals with a comparative analysis of three widely used data analysis methods: logistic regression, random forest, and neural networks. These methods have been evaluated in terms of accuracy, and computational efficiency and applied to different types of data sets, including both simulated and real MaaS data. The study aims to compare the efficiency of each method in classification tasks. The study leads to specific recommendations on which method to use under various circumstances, contributing to the decision-making process in data analysis projects. We have shown that random forests generally provide better accuracy and are resistant to over-training. Neural networks can achieve comparable performance under certain conditions, although at a high computational cost. Logistic regression shows limitations in dealing with complex data structures.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
2024 Smart City Symposium Prague (SCSP)
ISBN
979-8-3503-6095-0
ISSN
2831-5618
e-ISSN
2691-3666
Počet stran výsledku
5
Strana od-do
1-5
Název nakladatele
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Místo vydání
New York
Místo konání akce
Prague
Datum konání akce
23. 5. 2024
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
001258546700014