Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances.
<p>Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances.</p>
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , , , , , , , |
| Published: |
2024
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1852026000490102784 |
|---|---|
| author | Gilson Yuuji Shimizu (19837946) |
| author2 | Michael Schrempf (19837949) Elen Almeida Romão (4772397) Stefanie Jauk (19837952) Diether Kramer (19837955) Peter P. Rainer (5961086) José Abrão Cardeal da Costa (19837958) João Mazzoncini de Azevedo-Marques (3737785) Sandro Scarpelini (4320544) Katia Mitiko Firmino Suzuki (19837961) Hilton Vicente César (19837964) Paulo Mazzoncini de Azevedo-Marques (9073344) |
| author2_role | author author author author author author author author author author author |
| author_facet | Gilson Yuuji Shimizu (19837946) Michael Schrempf (19837949) Elen Almeida Romão (4772397) Stefanie Jauk (19837952) Diether Kramer (19837955) Peter P. Rainer (5961086) José Abrão Cardeal da Costa (19837958) João Mazzoncini de Azevedo-Marques (3737785) Sandro Scarpelini (4320544) Katia Mitiko Firmino Suzuki (19837961) Hilton Vicente César (19837964) Paulo Mazzoncini de Azevedo-Marques (9073344) |
| author_role | author |
| dc.creator.none.fl_str_mv | Gilson Yuuji Shimizu (19837946) Michael Schrempf (19837949) Elen Almeida Romão (4772397) Stefanie Jauk (19837952) Diether Kramer (19837955) Peter P. Rainer (5961086) José Abrão Cardeal da Costa (19837958) João Mazzoncini de Azevedo-Marques (3737785) Sandro Scarpelini (4320544) Katia Mitiko Firmino Suzuki (19837961) Hilton Vicente César (19837964) Paulo Mazzoncini de Azevedo-Marques (9073344) |
| dc.date.none.fl_str_mv | 2024-10-11T17:24:16Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0311719.g014 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Boxplots_of_Shapley_values_for_attributes_preselected_by_Boruta_method_in_a_subsample_of_600_instances_/27212813 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Biology Cancer Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified preto medical school evaluated regarding accuracy applied towards insights 882 ); accuracy 792 ); accuracy 859 &# 8211 782 &# 8211 778 &# 8211 704 &# 8211 support vector machine based risk prediction ribeir &# 227 xlink "> studies xlink "> among machine learning algorithms shapley values suggest rpms ), university random forest showed bidmc ), usa best predictive performance roc curve ). best generalization ability 000 mace cases local interpretability analyses interpretability </ p &# 227 xlink "> machine learning shapley values roc curve random forest predictive performance mace cases mace ). local interpretability interpretability analyses year risk good generalization 000 non retrospective cohort nearest neighbors naive bayes model reliability manuscript addresses layer perceptron final model decision tree consistent explanations cardiovascular diseases brazilian hospital balanced sample additional one 808 )) 717 )). |
| dc.title.none.fl_str_mv | Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_a87ed23cf11a7f99c4fea5decb49ce4e |
| identifier_str_mv | 10.1371/journal.pone.0311719.g014 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27212813 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances.Gilson Yuuji Shimizu (19837946)Michael Schrempf (19837949)Elen Almeida Romão (4772397)Stefanie Jauk (19837952)Diether Kramer (19837955)Peter P. Rainer (5961086)José Abrão Cardeal da Costa (19837958)João Mazzoncini de Azevedo-Marques (3737785)Sandro Scarpelini (4320544)Katia Mitiko Firmino Suzuki (19837961)Hilton Vicente César (19837964)Paulo Mazzoncini de Azevedo-Marques (9073344)Cell BiologyCancerScience PolicyPlant BiologyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedpreto medical schoolevaluated regarding accuracyapplied towards insights882 ); accuracy792 ); accuracy859 &# 8211782 &# 8211778 &# 8211704 &# 8211support vector machinebased risk predictionribeir &# 227xlink "> studiesxlink "> amongmachine learning algorithmsshapley values suggestrpms ), universityrandom forest showedbidmc ), usabest predictive performanceroc curve ).best generalization ability000 mace caseslocal interpretability analysesinterpretability </ p&# 227xlink ">machine learningshapley valuesroc curverandom forestpredictive performancemace casesmace ).local interpretabilityinterpretability analysesyear riskgood generalization000 nonretrospective cohortnearest neighborsnaive bayesmodel reliabilitymanuscript addresseslayer perceptronfinal modeldecision treeconsistent explanationscardiovascular diseasesbrazilian hospitalbalanced sampleadditional one808 ))717 )).<p>Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances.</p>2024-10-11T17:24:16ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0311719.g014https://figshare.com/articles/figure/Boxplots_of_Shapley_values_for_attributes_preselected_by_Boruta_method_in_a_subsample_of_600_instances_/27212813CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/272128132024-10-11T17:24:16Z |
| spellingShingle | Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances. Gilson Yuuji Shimizu (19837946) Cell Biology Cancer Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified preto medical school evaluated regarding accuracy applied towards insights 882 ); accuracy 792 ); accuracy 859 &# 8211 782 &# 8211 778 &# 8211 704 &# 8211 support vector machine based risk prediction ribeir &# 227 xlink "> studies xlink "> among machine learning algorithms shapley values suggest rpms ), university random forest showed bidmc ), usa best predictive performance roc curve ). best generalization ability 000 mace cases local interpretability analyses interpretability </ p &# 227 xlink "> machine learning shapley values roc curve random forest predictive performance mace cases mace ). local interpretability interpretability analyses year risk good generalization 000 non retrospective cohort nearest neighbors naive bayes model reliability manuscript addresses layer perceptron final model decision tree consistent explanations cardiovascular diseases brazilian hospital balanced sample additional one 808 )) 717 )). |
| status_str | publishedVersion |
| title | Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances. |
| title_full | Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances. |
| title_fullStr | Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances. |
| title_full_unstemmed | Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances. |
| title_short | Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances. |
| title_sort | Boxplots of Shapley values for attributes preselected by Boruta method in a subsample of 600 instances. |
| topic | Cell Biology Cancer Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified preto medical school evaluated regarding accuracy applied towards insights 882 ); accuracy 792 ); accuracy 859 &# 8211 782 &# 8211 778 &# 8211 704 &# 8211 support vector machine based risk prediction ribeir &# 227 xlink "> studies xlink "> among machine learning algorithms shapley values suggest rpms ), university random forest showed bidmc ), usa best predictive performance roc curve ). best generalization ability 000 mace cases local interpretability analyses interpretability </ p &# 227 xlink "> machine learning shapley values roc curve random forest predictive performance mace cases mace ). local interpretability interpretability analyses year risk good generalization 000 non retrospective cohort nearest neighbors naive bayes model reliability manuscript addresses layer perceptron final model decision tree consistent explanations cardiovascular diseases brazilian hospital balanced sample additional one 808 )) 717 )). |