0587093 20250317090640.720240621235959.9 85197315663 001258546700014 10.1109/SCSP61506.2024.10552715 5 s. P cav_un_epca*0587092979-8-3503-6096-72831-5618DanversIEEE2024 classification algorithms data analysis machine learning Mobility as a Service random forests cav_un_auth*0452994 Reznychenko T. CZ cav_un_auth*0383037 Uglickich Evženie UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2024/ZS/uglickich-0587093.pdf https://ieeexplore.ieee.org/document/10552715 8A21009 GA MŠk cav_un_auth*0432581 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. cav_un_auth*0468931 Smart City Symposium Prague 2024 (SCSP 2024) 20240523 20240524 Prague CZ BB 10000 10100 10103 2025 3 PR RVO:67985556 https://hdl.handle.net/11104/0354390 1 S 2024 85197315663 SCOPUS PUBMED 001258546700014 WOS pdf cav_un_epca*0587092 2024 Smart City Symposium Prague (SCSP) IEEE 2024 Danvers 979-8-3503-6096-7 2831-5618 2691-3666