Evaluation of Machine Learning Performance.
<div><p>This paper aims to examine the influence mechanism of education level as a key situational factor in the relationship between risk perception and behavioral response, encompassing both behavioral intention and preparatory behavior. Utilizing non-parametric estimation techniques i...
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2025
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| _version_ | 1852021575981727744 |
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| author | Zhipeng Wei (1418458) |
| author2 | Zhichun Zhang (713603) Liping Guo (596697) Wenjie Zhou (4004717) Kehu Yang (192609) |
| author2_role | author author author author |
| author_facet | Zhipeng Wei (1418458) Zhichun Zhang (713603) Liping Guo (596697) Wenjie Zhou (4004717) Kehu Yang (192609) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zhipeng Wei (1418458) Zhichun Zhang (713603) Liping Guo (596697) Wenjie Zhou (4004717) Kehu Yang (192609) |
| dc.date.none.fl_str_mv | 2025-04-03T17:25:50Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0321153.t002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Evaluation_of_Machine_Learning_Performance_/28725121 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Sociology Developmental Biology Science Policy Mental Health Biological Sciences not elsewhere classified statistical testing corroborates robust scientific foundation parametric estimation techniques model &# 8217 key situational factor 27 influencing factors xlink "> xgboost algorithms utilizing non significant influence risk perception random forest preparatory behavior paper aims machine learning influence mechanism findings indicate education level behavioral response behavioral intention 20 %, |
| dc.title.none.fl_str_mv | Evaluation of Machine Learning Performance. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>This paper aims to examine the influence mechanism of education level as a key situational factor in the relationship between risk perception and behavioral response, encompassing both behavioral intention and preparatory behavior. Utilizing non-parametric estimation techniques in machine learning, particularly the Random Forest and XGBoost algorithms, this study develops predictive models to analyze the impact of 27 influencing factors on behavioral responses following risk perception. The findings indicate that, while the model’s fit for preparatory behavior is 25.71% and its fit for behavioral intention is below 20%, the model effectively identifies key influencing factors. Further analysis employing SHAP values demonstrates that education level not only exerts a significant influence but also exhibits varying effects across different educational groups. Moreover, statistical testing corroborates the importance of education level in the relationship between risk perception and behavioral response, providing a robust scientific foundation for the development of risk management policies.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_b133dfe6ffd6c1bc18d2f3607ecb14e9 |
| identifier_str_mv | 10.1371/journal.pone.0321153.t002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28725121 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Evaluation of Machine Learning Performance.Zhipeng Wei (1418458)Zhichun Zhang (713603)Liping Guo (596697)Wenjie Zhou (4004717)Kehu Yang (192609)SociologyDevelopmental BiologyScience PolicyMental HealthBiological Sciences not elsewhere classifiedstatistical testing corroboratesrobust scientific foundationparametric estimation techniquesmodel &# 8217key situational factor27 influencing factorsxlink ">xgboost algorithmsutilizing nonsignificant influencerisk perceptionrandom forestpreparatory behaviorpaper aimsmachine learninginfluence mechanismfindings indicateeducation levelbehavioral responsebehavioral intention20 %,<div><p>This paper aims to examine the influence mechanism of education level as a key situational factor in the relationship between risk perception and behavioral response, encompassing both behavioral intention and preparatory behavior. Utilizing non-parametric estimation techniques in machine learning, particularly the Random Forest and XGBoost algorithms, this study develops predictive models to analyze the impact of 27 influencing factors on behavioral responses following risk perception. The findings indicate that, while the model’s fit for preparatory behavior is 25.71% and its fit for behavioral intention is below 20%, the model effectively identifies key influencing factors. Further analysis employing SHAP values demonstrates that education level not only exerts a significant influence but also exhibits varying effects across different educational groups. Moreover, statistical testing corroborates the importance of education level in the relationship between risk perception and behavioral response, providing a robust scientific foundation for the development of risk management policies.</p></div>2025-04-03T17:25:50ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0321153.t002https://figshare.com/articles/dataset/Evaluation_of_Machine_Learning_Performance_/28725121CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/287251212025-04-03T17:25:50Z |
| spellingShingle | Evaluation of Machine Learning Performance. Zhipeng Wei (1418458) Sociology Developmental Biology Science Policy Mental Health Biological Sciences not elsewhere classified statistical testing corroborates robust scientific foundation parametric estimation techniques model &# 8217 key situational factor 27 influencing factors xlink "> xgboost algorithms utilizing non significant influence risk perception random forest preparatory behavior paper aims machine learning influence mechanism findings indicate education level behavioral response behavioral intention 20 %, |
| status_str | publishedVersion |
| title | Evaluation of Machine Learning Performance. |
| title_full | Evaluation of Machine Learning Performance. |
| title_fullStr | Evaluation of Machine Learning Performance. |
| title_full_unstemmed | Evaluation of Machine Learning Performance. |
| title_short | Evaluation of Machine Learning Performance. |
| title_sort | Evaluation of Machine Learning Performance. |
| topic | Sociology Developmental Biology Science Policy Mental Health Biological Sciences not elsewhere classified statistical testing corroborates robust scientific foundation parametric estimation techniques model &# 8217 key situational factor 27 influencing factors xlink "> xgboost algorithms utilizing non significant influence risk perception random forest preparatory behavior paper aims machine learning influence mechanism findings indicate education level behavioral response behavioral intention 20 %, |