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Leave-one-out cross validation and Bootstrap .632+ estimator errors per features set.

Sara Ramella (5204864)
Michele Fiore (361256)
Carlo Greco (5204846)
Ermanno Cordelli (4419739)
Rosa Sicilia (6000515)
Mario Merone (6000518)
Elisabetta Molfese (6000521)
Marianna Miele (6000524)
Patrizia Cornacchione (5204849)
Edy Ippolito (5204858)
Giulio Iannello (4419733)
Rolando Maria D’Angelillo (5204867)
Paolo Soda (4419736)

Publication date

November 2018

DOI

10.1371/journal.pone.0207455.t003

Publisher

Public Library of Science (PLoS)

Abstract

Leave-one-out cross validation and Bootstrap .632+ estimator errors per features set.</p

Extracted data

Related items

AUC in the framework of leave-one-out cross validation schema under different weight parameters is calculated to confirm that miREFScan is robust to the selection of parameter values.

Xing Chen (140292)
Ming-Xi Liu (140293)
Qing-Hua Cui (140294)
Gui-Ying Yan (140295)

August 2012

<p>AUC in the framework of leave-one-out cross validation schema under different weight parameters i...

AUC in the framework of leave-one-out cross validation schema under different trade-off parameters combination is calculated to confirm that miREFScan is robust to the selection of parameter values.

Xing Chen (140292)
Ming-Xi Liu (140293)
Qing-Hua Cui (140294)
Gui-Ying Yan (140295)

August 2012

<p>AUC in the framework of leave-one-out cross validation schema under different trade-off parameter...

Feature Selection (mean±std over 100 10-fold cross-validations, i.e. 1000 classification models).

Yu Shimizu (733328)
Junichiro Yoshimoto (253281)
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Masahiro Takamura (733329)
Shinpei Yoshimura (344274)
Yasumasa Okamoto (82701)
Shigeto Yamawaki (82702)
Kenji Doya (82703)

May 2015

<p>Feature Selection (mean±std over 100 10-fold cross-validations, i.e. 1000 classification models)....

Performances of leave one feature out validations and using all six features on NS800 (5-fold cross validation, %).

Wusong Mao (501294)
Peisheng Cong (121213)
Zhiheng Wang (424546)
Longjian Lu (501295)
Zhongliang Zhu (487392)
Tonghua Li (121211)

December 2013

<p>Performances of leave one feature out validations and using all six features on NS800 (5-fold cro...

Leave-out cross validation data for <i>T</i>.<i>cruzi</i> Bayesian models.

Sean Ekins (162)
Jair Lage de Siqueira-Neto (761585)
Laura-Isobel McCall (222498)
Malabika Sarker (14291)
Maneesh Yadav (761586)
Elizabeth L. Ponder (256410)
E. Adam Kallel (761587)
Danielle Kellar (701997)
Steven Chen (145741)
Michelle Arkin (694024)
Barry A. Bunin (410987)
James H. McKerrow (113918)
Carolyn Talcott (3286659)

June 2015

<p>Leave-out cross validation data for <i>T</i>.<i>cruzi</i> Bayesian models.</p

Standard error and average value for each estimate after bootstrapping for example 2.

Majd Alslman (14123898)
Amal Helu (12536797)

November 2022

Standard error and average value for each estimate after bootstrapping for example 2.</p

Accuracy measures for 10-fold cross-validation of Model 1 using the entire feature set for prediction.

Zhila Esna Ashari (5194481)
Kelly A. Brayton (112912)
Shira L. Broschat (167647)

January 2019

Accuracy measures for 10-fold cross-validation of Model 1 using the entire feature set for predictio...

The leave-one-out cross-validation results for each model in the qpure method.

Sarah Song (132618)
Katia Nones (132620)
David Miller (62440)
Ivon Harliwong (132623)
Karin S. Kassahn (132626)
Mark Pinese (52475)
Marina Pajic (132629)
Anthony J. Gill (132630)
Amber L. Johns (132632)
Matthew Anderson (132634)
Oliver Holmes (132636)
Conrad Leonard (132638)
Darrin Taylor (16118)
Scott Wood (132640)
Qinying Xu (132643)
Felicity Newell (132648)
Mark J. Cowley (107519)
Jianmin Wu (19537)
Peter Wilson (132656)
Lynn Fink (132661)
Andrew V. Biankin (81390)
Nic Waddell (16119)
Sean M. Grimmond (132666)
John V. Pearson (67320)

September 2012

<p>In the second column the number in the brackets is the pre-defined number of components. The smal...

Cross-validation feature selection evaluation results for buffer_overflow data.

Guangyu Zhao (180047)
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Tianyu Lan (5139758)
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Cross-validation feature selection evaluation results for buffer_overflow data.</p

Accuracy of cross-validations using different parameters.

Nitesh Kumar Singh (469729)
Dirk Repsilber (21257)
Volkmar Liebscher (469730)
Leila Taher (179979)
Georg Fuellen (16022)

October 2013

<p>A) Accuracy computed under scenario 1 (see Methods). B) Accuracy computed under scenario 2 (see M...

Prediction performance of leave-one-out cross-validation based on different encoding methods.

Yong-Zi Chen (158654)
Zhen Chen (129176)
Yu-Ai Gong (158656)
Guoguang Ying (158659)

June 2012

<p>Prediction performance of leave-one-out cross-validation based on different encoding methods.</p

10-Fold Cross Validation Accuracies of the classifiers applied to the Artificial dataset.

Orlando Anunciação (475568)
Susana Vinga (40513)
Arlindo L. Oliveira (13792)

October 2013

<p>10-Fold Cross Validation Accuracies of the classifiers applied to the Artificial dataset.</p

Leave-one-out cross validation where one of the total samples was removed, a prediction model was developed using the remaining samples, and the left-out sample was then predicted for sphere or adherent cells in each platform.

Won Jun Lee (517835)
Sang Cheol Kim (137613)
Jung-Ho Yoon (1805689)
Sang Jun Yoon (1805686)
Johan Lim (517839)
You-Sun Kim (236014)
Sung Won Kwon (498034)
Jeong Hill Park (517837)

February 2016

<p>An output of 1 indicates that a left-out sample from adherent or sphere cells was predicted to be...

10-Fold Cross Validation Accuracy of classification methods with the addition of noisy variables.

Orlando Anunciação (475568)
Susana Vinga (40513)
Arlindo L. Oliveira (13792)

October 2013

<p>10-Fold Cross Validation Accuracy of classification methods with the addition of noisy variables....

The performance of the AdaBoost classifier with 6 selected features using the Leave-one-subject-out cross-validation method.

Rui Cao (800989)
Amir M. Rahmani (10090518)
Karen L. Lindsay (8395599)

September 2022

The performance of the AdaBoost classifier with 6 selected features using the Leave-one-subject-out ...

AUC in the framework of leave-one-out cross validation schema under different weight parameters is calculated to confirm that miREFScan is robust to the selection of parameter values.

Xing Chen (140292)
Ming-Xi Liu (140293)
Qing-Hua Cui (140294)
Gui-Ying Yan (140295)

August 2012

<p>AUC in the framework of leave-one-out cross validation schema under different weight parameters i...

AUC in the framework of leave-one-out cross validation schema under different trade-off parameters combination is calculated to confirm that miREFScan is robust to the selection of parameter values.

Xing Chen (140292)
Ming-Xi Liu (140293)
Qing-Hua Cui (140294)
Gui-Ying Yan (140295)

August 2012

<p>AUC in the framework of leave-one-out cross validation schema under different trade-off parameter...

Feature Selection (mean±std over 100 10-fold cross-validations, i.e. 1000 classification models).

Yu Shimizu (733328)
Junichiro Yoshimoto (253281)
Shigeru Toki (344277)
Masahiro Takamura (733329)
Shinpei Yoshimura (344274)
Yasumasa Okamoto (82701)
Shigeto Yamawaki (82702)
Kenji Doya (82703)

May 2015

<p>Feature Selection (mean±std over 100 10-fold cross-validations, i.e. 1000 classification models)....

Performances of leave one feature out validations and using all six features on NS800 (5-fold cross validation, %).

Wusong Mao (501294)
Peisheng Cong (121213)
Zhiheng Wang (424546)
Longjian Lu (501295)
Zhongliang Zhu (487392)
Tonghua Li (121211)

December 2013

<p>Performances of leave one feature out validations and using all six features on NS800 (5-fold cro...

Leave-out cross validation data for <i>T</i>.<i>cruzi</i> Bayesian models.

Sean Ekins (162)
Jair Lage de Siqueira-Neto (761585)
Laura-Isobel McCall (222498)
Malabika Sarker (14291)
Maneesh Yadav (761586)
Elizabeth L. Ponder (256410)
E. Adam Kallel (761587)
Danielle Kellar (701997)
Steven Chen (145741)
Michelle Arkin (694024)
Barry A. Bunin (410987)
James H. McKerrow (113918)
Carolyn Talcott (3286659)

June 2015

<p>Leave-out cross validation data for <i>T</i>.<i>cruzi</i> Bayesian models.</p

Standard error and average value for each estimate after bootstrapping for example 2.

Majd Alslman (14123898)
Amal Helu (12536797)

November 2022

Standard error and average value for each estimate after bootstrapping for example 2.</p

Accuracy measures for 10-fold cross-validation of Model 1 using the entire feature set for prediction.

Zhila Esna Ashari (5194481)
Kelly A. Brayton (112912)
Shira L. Broschat (167647)

January 2019

Accuracy measures for 10-fold cross-validation of Model 1 using the entire feature set for predictio...

The leave-one-out cross-validation results for each model in the qpure method.

Sarah Song (132618)
Katia Nones (132620)
David Miller (62440)
Ivon Harliwong (132623)
Karin S. Kassahn (132626)
Mark Pinese (52475)
Marina Pajic (132629)
Anthony J. Gill (132630)
Amber L. Johns (132632)
Matthew Anderson (132634)
Oliver Holmes (132636)
Conrad Leonard (132638)
Darrin Taylor (16118)
Scott Wood (132640)
Qinying Xu (132643)
Felicity Newell (132648)
Mark J. Cowley (107519)
Jianmin Wu (19537)
Peter Wilson (132656)
Lynn Fink (132661)
Andrew V. Biankin (81390)
Nic Waddell (16119)
Sean M. Grimmond (132666)
John V. Pearson (67320)

September 2012

<p>In the second column the number in the brackets is the pre-defined number of components. The smal...

Cross-validation feature selection evaluation results for buffer_overflow data.

Guangyu Zhao (180047)
Peng Liu (120506)
Ke Sun (542534)
Yang Yang (45629)
Tianyu Lan (5139758)
Han Yang (704814)

October 2023

Cross-validation feature selection evaluation results for buffer_overflow data.</p

Accuracy of cross-validations using different parameters.

Nitesh Kumar Singh (469729)
Dirk Repsilber (21257)
Volkmar Liebscher (469730)
Leila Taher (179979)
Georg Fuellen (16022)

October 2013

<p>A) Accuracy computed under scenario 1 (see Methods). B) Accuracy computed under scenario 2 (see M...

Prediction performance of leave-one-out cross-validation based on different encoding methods.

Yong-Zi Chen (158654)
Zhen Chen (129176)
Yu-Ai Gong (158656)
Guoguang Ying (158659)

June 2012

<p>Prediction performance of leave-one-out cross-validation based on different encoding methods.</p

10-Fold Cross Validation Accuracies of the classifiers applied to the Artificial dataset.

Orlando Anunciação (475568)
Susana Vinga (40513)
Arlindo L. Oliveira (13792)

October 2013

<p>10-Fold Cross Validation Accuracies of the classifiers applied to the Artificial dataset.</p

Leave-one-out cross validation where one of the total samples was removed, a prediction model was developed using the remaining samples, and the left-out sample was then predicted for sphere or adherent cells in each platform.

Won Jun Lee (517835)
Sang Cheol Kim (137613)
Jung-Ho Yoon (1805689)
Sang Jun Yoon (1805686)
Johan Lim (517839)
You-Sun Kim (236014)
Sung Won Kwon (498034)
Jeong Hill Park (517837)

February 2016

<p>An output of 1 indicates that a left-out sample from adherent or sphere cells was predicted to be...

10-Fold Cross Validation Accuracy of classification methods with the addition of noisy variables.

Orlando Anunciação (475568)
Susana Vinga (40513)
Arlindo L. Oliveira (13792)

October 2013

<p>10-Fold Cross Validation Accuracy of classification methods with the addition of noisy variables....

The performance of the AdaBoost classifier with 6 selected features using the Leave-one-subject-out cross-validation method.

Rui Cao (800989)
Amir M. Rahmani (10090518)
Karen L. Lindsay (8395599)

September 2022

The performance of the AdaBoost classifier with 6 selected features using the Leave-one-subject-out ...

AUC in the framework of leave-one-out cross validation schema under different weight parameters is calculated to confirm that miREFScan is robust to the selection of parameter values.

Xing Chen (140292)
Ming-Xi Liu (140293)
Qing-Hua Cui (140294)
Gui-Ying Yan (140295)

August 2012

<p>AUC in the framework of leave-one-out cross validation schema under different weight parameters i...

AUC in the framework of leave-one-out cross validation schema under different trade-off parameters combination is calculated to confirm that miREFScan is robust to the selection of parameter values.

Xing Chen (140292)
Ming-Xi Liu (140293)
Qing-Hua Cui (140294)
Gui-Ying Yan (140295)

August 2012

<p>AUC in the framework of leave-one-out cross validation schema under different trade-off parameter...

Feature Selection (mean±std over 100 10-fold cross-validations, i.e. 1000 classification models).

Yu Shimizu (733328)
Junichiro Yoshimoto (253281)
Shigeru Toki (344277)
Masahiro Takamura (733329)
Shinpei Yoshimura (344274)
Yasumasa Okamoto (82701)
Shigeto Yamawaki (82702)
Kenji Doya (82703)

May 2015

<p>Feature Selection (mean±std over 100 10-fold cross-validations, i.e. 1000 classification models)....