Accuracy Comparison of Logistic Regression, Random Forest, and Neural Networks Applied to Real MaaS Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00587093" target="_blank" >RIV/67985556:_____/24:00587093 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10552715" target="_blank" >https://ieeexplore.ieee.org/document/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>

Result language
angličtina
Original language name
Accuracy Comparison of Logistic Regression, Random Forest, and Neural Networks Applied to Real MaaS Data
Original language description
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.
Czech name
—
Czech description
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Project
<a href="/en/project/8A21009" target="_blank" >8A21009: Embedded storage elements on next MCU generation ready for AI on the edge</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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