交通堵塞的机器学习分类算法--比较研究

Mërgim H. Hoti, Elvir Misini, Uran Lajçi, L. Ahmedi
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引用次数: 0

摘要

机器学习算法在各个领域的应用势头日渐强劲,其结果日益凸显出进一步测试和实施的必要性。这是因为机器学习算法具有简化和加快众多流程的潜力。在本文中,我们采用了五种算法:KNN、决策树、随机森林、逻辑回归和 Naive Bayes,并在三个大型数据集中对这些算法进行了测试。平均而言,它们的性能从最低 80% 到最高 90% 不等。数据预处理已经完成,与此同时,我们还实施了 SMOTE 算法,以应对本研究中不平衡数据的挑战。同时,对于 "is_arrested "类,Naïve Bayes 算法在准确度、精确度、召回率和 F1 分数方面都取得了最理想的结果。此外,为了评估每种算法的性能,我们采用了包括准确率、精确率、召回率和 F1 分数在内的指标。通过这些指标,我们可以确定哪种算法实现了最有效的分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Classification Algorithms for Traffic Stops—A Comparative Study
The application of machine learning algorithms across various fields is gaining momentum, and the results increasingly emphasize the need for further testing and implementation. This is driven by the potential to streamline and expedite numerous processes. In this paper, we have employed five algorithms: KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes, and these algorithms have been tested in three large datasets. On average, their performance ranges from a minimum of 80% to a maximum of 90%. Data preprocessing has been completed, and concurrently, we have implemented the SMOTE algorithm to address the challenge of unbalanced data in this research. Simultaneously, the Naïve Bayes algorithm yields the most favorable results of Accuracy, Precision, Recall, and F1 Score, for the “is_arrested” class. Furthermore, to assess the performance of each algorithm, we employed metrics including Accuracy, Precision, Recall, and F1 Score. These metrics allowed us to decide which algorithm achieved the most effective classification.
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