[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121996-en":3,"doc-seo-121996-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121996,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Machine Learning Classification Algorithms for Traffic Stops - A Comparative Study","Machine Learning Classification Algorithms for Traffic Stops examines how supervised learning can improve the identification and prediction tasks associated with traffic stop outcomes. The study evaluates five algorithms—KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes—on three large datasets after completing preprocessing and addressing class imbalance with SMOTE. Performance is assessed with Accuracy, Precision, Recall, and F1 Score to determine the most effective model.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 20 No. 7 (2024) |   \n[https://doi.org/10.3991/ijoe.v20i07.47763](https://doi.org/10.3991/ijoe.v20i07.47763)  \nPAPER  \nMachine Learning Classification Algorithms for Traffic Stops—A Comparative Study  \nMërgim H. Hoti(􀀍), Elvir Misini, Uran Lajçi, LuleAhmedi  \nUniversity of Prishtina, Prishtine, Republic of Kosovo [mergim.hoti@uni-pr.edu](mergim.hoti@uni-pr.edu)  \nABSTRACT  \nThe 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.  \nKEYWORDS  \nsupervised algorithms, classification algorithms, traffic stops, Accuracy, Precision, etc  \n1 INTRODUCTION  \nEvery day, we see a growing trend of increasing traffic offenses and mistakes, despite advancements in vehicle self-control capabilities, ranging from simpler situations to interventions in more critical moments. Disregarding traffic signs directly contributes to problems that can result in the fatalities of traffic participants.  \nHence, the utilization of machine learning algorithms in this context serves as an additional factor for enhancing performance in efforts to reduce instances of traffic accidents and violence. Furthermore, through the accurate identification of these factors, regulations and law enforcement can be tailored and refined to ensure the safety of every traffic participant.  \nIn this paper, various cases are examined using classes from three datasets comprising 386,452 rows, sourced from [1]. This data has been gathered from the states  \nHoti, M. H., Misini, E., Lajçi, U., Ahmedi, L. (2024) . Machine Learning Classification Algorithms for Traffic Stops—A Comparative Study. International Journal of Online and Biomedical Engineering (iJOE), 20(7), pp. 18–29. [https://doi.org/10.3991/ijoe.v20i07.47763](https://doi.org/10.3991/ijoe.v20i07.47763)[ ](https://doi.org/10.3991/ijoe.v20i07.47763)[Article submitted 2024-01-04. Revision uploaded 2024-02-13. Final acceptance 2024-02-14.](Article submitted 2024-01-04. Revision uploaded 2024-02-13. Final acceptance 2024-02-14.)  \n© 2024 by the authors of this article. Published under CC-BY.  \n18 International Journal of Online and Biomedical Engineering (iJOE) iJOE | Vol. 20 No. 7 (2024)  \nMachine Learning Classification Algorithms for Traffic Stops—A Comparative Study  \nof Stockton, Durham and Burlington, which encompass varying amounts of data with classes including age, gender, and their arrest status, among others. In this study, we address five distinct classes (age, race, sex, search conducted, outcome, and arrest) to assess their significance and evaluate how effectively the selected algorithms can predict them. At the same time, we have chosen five distinct algorithms: K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Logistic Regression, and Naive Bayes.  \nThe data are taken from the Stanford Open Policing Project [1], a","cbCaiadWA1qidhCI","https://ap.wps.com/l/cbCaiadWA1qidhCI","pdf",488086,1,12,"English","en",105,"# Introduction\n# Related Work\n# Methodology\n# Results and Discussion\n# Conclusion","[{\"question\":\"Which machine learning algorithms are evaluated for traffic stop classification?\",\"answer\":\"The paper tests five supervised algorithms: KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes.\"},{\"question\":\"How does the study handle imbalanced data?\",\"answer\":\"Data preprocessing is performed and SMOTE is implemented to address the unbalanced class distribution in the research.\"},{\"question\":\"Which metrics are used to compare model performance?\",\"answer\":\"Model performance is evaluated using Accuracy, Precision, Recall, and F1 Score, which are used to decide the most effective classification approach.\"}]","Machine Learning Classification Algorithms for Traffic Stops - 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