The goodness of fit criteria results in all algorithms for optimal hyperparameter values.
<p>The goodness of fit criteria results in all algorithms for optimal hyperparameter values.</p>
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2025
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| _version_ | 1852015926333931520 |
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| author | Sevtap Tırınk (22394716) |
| author_facet | Sevtap Tırınk (22394716) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sevtap Tırınk (22394716) |
| dc.date.none.fl_str_mv | 2025-10-08T18:00:11Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0334252.t005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/The_goodness_of_fit_criteria_results_in_all_algorithms_for_optimal_hyperparameter_values_/30310622 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified threatens environmental sustainability svm )&# 8212 suggest practical implications pollutant data collected mean absolute error highest prediction accuracy extreme gradient boosting early warning systems monitoring air quality air quality index &# 8323 ;) &# 8322 ;, term environmental patterns support vector machine wind speed ). svm </ p five meteorological variables air pollution &# 252 wind direction term assessment machine learning 158 ). take precautions study presents results demonstrate relative humidity performance metrics global problem evaluated using enabling individuals comparative approach based forecasting |
| dc.title.none.fl_str_mv | The goodness of fit criteria results in all algorithms for optimal hyperparameter values. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>The goodness of fit criteria results in all algorithms for optimal hyperparameter values.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_b1ea7637dd7a13529f5df2f2f531ee2d |
| identifier_str_mv | 10.1371/journal.pone.0334252.t005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30310622 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The goodness of fit criteria results in all algorithms for optimal hyperparameter values.Sevtap Tırınk (22394716)EcologyScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedthreatens environmental sustainabilitysvm )&# 8212suggest practical implicationspollutant data collectedmean absolute errorhighest prediction accuracyextreme gradient boostingearly warning systemsmonitoring air qualityair quality index&# 8323 ;)&# 8322 ;,term environmental patternssupport vector machinewind speed ).svm </ pfive meteorological variablesair pollution&# 252wind directionterm assessmentmachine learning158 ).take precautionsstudy presentsresults demonstraterelative humidityperformance metricsglobal problemevaluated usingenabling individualscomparative approachbased forecasting<p>The goodness of fit criteria results in all algorithms for optimal hyperparameter values.</p>2025-10-08T18:00:11ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0334252.t005https://figshare.com/articles/dataset/The_goodness_of_fit_criteria_results_in_all_algorithms_for_optimal_hyperparameter_values_/30310622CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303106222025-10-08T18:00:11Z |
| spellingShingle | The goodness of fit criteria results in all algorithms for optimal hyperparameter values. Sevtap Tırınk (22394716) Ecology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified threatens environmental sustainability svm )&# 8212 suggest practical implications pollutant data collected mean absolute error highest prediction accuracy extreme gradient boosting early warning systems monitoring air quality air quality index &# 8323 ;) &# 8322 ;, term environmental patterns support vector machine wind speed ). svm </ p five meteorological variables air pollution &# 252 wind direction term assessment machine learning 158 ). take precautions study presents results demonstrate relative humidity performance metrics global problem evaluated using enabling individuals comparative approach based forecasting |
| status_str | publishedVersion |
| title | The goodness of fit criteria results in all algorithms for optimal hyperparameter values. |
| title_full | The goodness of fit criteria results in all algorithms for optimal hyperparameter values. |
| title_fullStr | The goodness of fit criteria results in all algorithms for optimal hyperparameter values. |
| title_full_unstemmed | The goodness of fit criteria results in all algorithms for optimal hyperparameter values. |
| title_short | The goodness of fit criteria results in all algorithms for optimal hyperparameter values. |
| title_sort | The goodness of fit criteria results in all algorithms for optimal hyperparameter values. |
| topic | Ecology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified threatens environmental sustainability svm )&# 8212 suggest practical implications pollutant data collected mean absolute error highest prediction accuracy extreme gradient boosting early warning systems monitoring air quality air quality index &# 8323 ;) &# 8322 ;, term environmental patterns support vector machine wind speed ). svm </ p five meteorological variables air pollution &# 252 wind direction term assessment machine learning 158 ). take precautions study presents results demonstrate relative humidity performance metrics global problem evaluated using enabling individuals comparative approach based forecasting |