The 95% confidence intervals of accuracy and MCC of the supervised learning models for the main dataset.

Performance of machine learning models on test set using the SMOTE-adjusted balanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the SMOTE-adjusted balanced training set.</...

Performance of machine learning models on test set using the original imbalanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the original imbalanced training set.</p

The average precision for active learning versus most confident (MC) prediction selection.

Ibrahim Burak Ozyurt (843371)
Jeffrey S. Grethe (798468)
Maryann E. Martone (798470)
Anita E. Bandrowski (843372)

January 2016

The average precision for active learning versus most confident (MC) prediction selection.</p

The 95% confidence intervals of accuracy and MCC of the supervised learning models for the external dataset.

Siddharth Guha (17896541)
Abdalla Ibrahim (10765778)
Qian Wu (209382)
Pengfei Geng (14441652)
Yen Chou (17896544)
Hao Yang (328526)
Jingchen Ma (586255)
Lin Lu (5759)
Delin Wang (4433131)
Lawrence H. Schwartz (3608102)
Chuan-miao Xie (5664082)
Binsheng Zhao (3608099)

February 2024

The 95% confidence intervals of accuracy and MCC of the supervised learning models for the external ...

The average accuracy, AUPRC, and MCC of the supervised learning models for the main dataset.

Siddharth Guha (17896541)
Abdalla Ibrahim (10765778)
Qian Wu (209382)
Pengfei Geng (14441652)
Yen Chou (17896544)
Hao Yang (328526)
Jingchen Ma (586255)
Lin Lu (5759)
Delin Wang (4433131)
Lawrence H. Schwartz (3608102)
Chuan-miao Xie (5664082)
Binsheng Zhao (3608099)

February 2024

The average accuracy, AUPRC, and MCC of the supervised learning models for the main dataset.</p

The average accuracy, AUPRC, and MCC of the supervised learning models for the external dataset.

Siddharth Guha (17896541)
Abdalla Ibrahim (10765778)
Qian Wu (209382)
Pengfei Geng (14441652)
Yen Chou (17896544)
Hao Yang (328526)
Jingchen Ma (586255)
Lin Lu (5759)
Delin Wang (4433131)
Lawrence H. Schwartz (3608102)
Chuan-miao Xie (5664082)
Binsheng Zhao (3608099)

February 2024

The average accuracy, AUPRC, and MCC of the supervised learning models for the external dataset.</p

Mean, upper 90% Confidence Interval and 95th percentiles for six surveillance datasets.

Ee Laine Tay (469784)
Kristina Grant (360313)
Martyn Kirk (469785)
Anthony Mounts (469786)
Heath Kelly (210683)

October 2013

Mean, upper 90% Confidence Interval and 95th percentiles for six surveillance datasets.</p

Parameter estimates and 95% credible intervals for the categorical variables of the chosen model.

James Chirombo (2225650)
Rachel Lowe (603632)
Lawrence Kazembe (388518)

July 2014

Parameter estimates and 95% credible intervals for the categorical variables of the chosen model....

Log-weight accuracy rates for the machine learning and linear regression models.

Oscar Miguel-Hurtado (3328776)
Richard Guest (700705)
Sarah V. Stevenage (3328785)
Greg J. Neil (3328779)
Sue Black (3328782)

November 2016

Log-weight accuracy rates for the machine learning and linear regression models.</p

Confidence intervals (95%) for estimates of mu, with the “best-fit” GSS model, based on the original (empirical) data set, from bootstrapped data sets and from simulations (where mu = 0).

Stephen W. Attwood (378029)
E. Suchart Upatham (378031)

November 2013

Confidence intervals (95%) for estimates of mu, with the “best-fit” GSS model, based on the origi...

Mean and 95% confidence intervals of the single variable models, for each scenario (pre- and post-PPCDAM).

Isabel M. D. Rosa (474208)
Drew Purves (362743)
Carlos Souza Jr (474209)
Robert M. Ewers (35039)

October 2013

Mean and 95% confidence intervals of the single variable models, for each scenario (pre- and post...

Model estimates of top model predicting the proportion of time spent vigilant, with 95% confidence intervals.

Julian C. Evans (5013503)
Sasha R. X. Dall (155660)
Caitlin R. Kight (6166958)

December 2018

Model estimates of top model predicting the proportion of time spent vigilant, with 95% confidence i...

Performance of machine learning models on test set using the ROSE-adjusted balanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the ROSE-adjusted balanced training set.</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...

Supervised learning Error estimation

Az U

December 2014

is th sed fficu r ba ce. omp ral p data are employed. Our simulations show the majority of the confi...

Performance of machine learning models on test set using the SMOTE-adjusted balanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the SMOTE-adjusted balanced training set.</...

Performance of machine learning models on test set using the original imbalanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the original imbalanced training set.</p

The average precision for active learning versus most confident (MC) prediction selection.

Ibrahim Burak Ozyurt (843371)
Jeffrey S. Grethe (798468)
Maryann E. Martone (798470)
Anita E. Bandrowski (843372)

January 2016

The average precision for active learning versus most confident (MC) prediction selection.</p

The 95% confidence intervals of accuracy and MCC of the supervised learning models for the external dataset.

Siddharth Guha (17896541)
Abdalla Ibrahim (10765778)
Qian Wu (209382)
Pengfei Geng (14441652)
Yen Chou (17896544)
Hao Yang (328526)
Jingchen Ma (586255)
Lin Lu (5759)
Delin Wang (4433131)
Lawrence H. Schwartz (3608102)
Chuan-miao Xie (5664082)
Binsheng Zhao (3608099)

February 2024

The 95% confidence intervals of accuracy and MCC of the supervised learning models for the external ...

The average accuracy, AUPRC, and MCC of the supervised learning models for the main dataset.

Siddharth Guha (17896541)
Abdalla Ibrahim (10765778)
Qian Wu (209382)
Pengfei Geng (14441652)
Yen Chou (17896544)
Hao Yang (328526)
Jingchen Ma (586255)
Lin Lu (5759)
Delin Wang (4433131)
Lawrence H. Schwartz (3608102)
Chuan-miao Xie (5664082)
Binsheng Zhao (3608099)

February 2024

The average accuracy, AUPRC, and MCC of the supervised learning models for the main dataset.</p

The average accuracy, AUPRC, and MCC of the supervised learning models for the external dataset.

Siddharth Guha (17896541)
Abdalla Ibrahim (10765778)
Qian Wu (209382)
Pengfei Geng (14441652)
Yen Chou (17896544)
Hao Yang (328526)
Jingchen Ma (586255)
Lin Lu (5759)
Delin Wang (4433131)
Lawrence H. Schwartz (3608102)
Chuan-miao Xie (5664082)
Binsheng Zhao (3608099)

February 2024

The average accuracy, AUPRC, and MCC of the supervised learning models for the external dataset.</p

Mean, upper 90% Confidence Interval and 95th percentiles for six surveillance datasets.

Ee Laine Tay (469784)
Kristina Grant (360313)
Martyn Kirk (469785)
Anthony Mounts (469786)
Heath Kelly (210683)

October 2013

Mean, upper 90% Confidence Interval and 95th percentiles for six surveillance datasets.</p

Parameter estimates and 95% credible intervals for the categorical variables of the chosen model.

James Chirombo (2225650)
Rachel Lowe (603632)
Lawrence Kazembe (388518)

July 2014

Parameter estimates and 95% credible intervals for the categorical variables of the chosen model....

Log-weight accuracy rates for the machine learning and linear regression models.

Oscar Miguel-Hurtado (3328776)
Richard Guest (700705)
Sarah V. Stevenage (3328785)
Greg J. Neil (3328779)
Sue Black (3328782)

November 2016

Log-weight accuracy rates for the machine learning and linear regression models.</p

Confidence intervals (95%) for estimates of mu, with the “best-fit” GSS model, based on the original (empirical) data set, from bootstrapped data sets and from simulations (where mu = 0).

Stephen W. Attwood (378029)
E. Suchart Upatham (378031)

November 2013

Confidence intervals (95%) for estimates of mu, with the “best-fit” GSS model, based on the origi...

Mean and 95% confidence intervals of the single variable models, for each scenario (pre- and post-PPCDAM).

Isabel M. D. Rosa (474208)
Drew Purves (362743)
Carlos Souza Jr (474209)
Robert M. Ewers (35039)

October 2013

Mean and 95% confidence intervals of the single variable models, for each scenario (pre- and post...

Model estimates of top model predicting the proportion of time spent vigilant, with 95% confidence intervals.

Julian C. Evans (5013503)
Sasha R. X. Dall (155660)
Caitlin R. Kight (6166958)

December 2018

Model estimates of top model predicting the proportion of time spent vigilant, with 95% confidence i...

Performance of machine learning models on test set using the ROSE-adjusted balanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the ROSE-adjusted balanced training set.</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...

Supervised learning Error estimation

Az U

December 2014

is th sed fficu r ba ce. omp ral p data are employed. Our simulations show the majority of the confi...

Performance of machine learning models on test set using the SMOTE-adjusted balanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the SMOTE-adjusted balanced training set.</...

Performance of machine learning models on test set using the original imbalanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the original imbalanced training set.</p

The average precision for active learning versus most confident (MC) prediction selection.

Ibrahim Burak Ozyurt (843371)
Jeffrey S. Grethe (798468)
Maryann E. Martone (798470)
Anita E. Bandrowski (843372)

January 2016

The average precision for active learning versus most confident (MC) prediction selection.</p

The 95% confidence intervals of accuracy and MCC of the supervised learning models for the main dataset.

Abstract

Extracted data

The 95% confidence intervals of accuracy and MCC of the supervised learning models for the main dataset.

Abstract

Extracted data

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