The prediction performance for Elastic Net, Random Forests and Support Vector Machines, as compared to the upper limit when randomly selecting one technical replicate as the prediction.

Prediction efficiencies of random forest and two neighbor-assisted methods.

Juyong Lee (399709)
Jooyoung Lee (399710)

April 2013

Prediction efficiencies are measured by AUC (area under the PR curve) and the maximum value of MC...

A summary of model performance in predicting spread across the three periods.

Justin Kaashoek (14216292)
Christian Testa (10007166)
Jarvis T. Chen (9541445)
Lucas M. Stolerman (7163918)
Nancy Krieger (275361)
William P. Hanage (9553184)
Mauricio Santillana (528516)

December 2022

We choose to evaluate model performance on R2 of residuals as well as the Mean Absolute Error. We al...

Performance metrics of the prediction models using logistic regression and random forest methods with five-fold cross-validation.

Fei Yang (46361)
Tong Meng (462237)
Ben Torben-Nielsen (14725376)
Carsten Magnus (327831)
Chuang Liu (165506)
Emilie Dejean (14725379)

March 2023

Performance metrics of the prediction models using logistic regression and random forest methods wit...

The prediction performances for drug-target features and chemical fingerprint features using Elastic Net.

Alina Malyutina (6714752)
Muntasir Mamun Majumder (6714755)
Wenyu Wang (800903)
Alberto Pessia (6714758)
Caroline A. Heckman (5339465)
Jing Tang (33607)

May 2019

RMSE: root mean square error, R2: coefficient of determination, COR: Pearson correlation, MAE: mean ...

Comparison of the prediction errors between two computational models.

Seungsoo Hahn (175873)
Dongsup Kim (50568)

March 2012

The prediction errors from the MS and SS models are plotted as circles and triangles, respectivel...

Prediction performance comparison.

Qianyun Li (528993)
Runmin Shi (6407282)
Faming Liang (186676)

February 2019

Comparison of the prediction performance (measured by corr(Ytest, )) of the mixture regression model...

Root mean squared error of prediction (RMSEP) for different number of components of partial least squares (PLS) repression.

Alamgir Kabir (548332)
Md. Jahanur Rahman (4707907)
Abu Ahmed Shamim (548334)
Rolf D. W. Klemm (4707904)
Alain B. Labrique (240434)
Mahbubur Rashid (240438)
Parul Christian (548336)
Keith P. West Jr. (240443)

December 2017

Root mean squared error of prediction (RMSEP) for different number of components of partial least...

Performance of multi-class predictions.

M. Umut Caglar (5925230)
Adam J. Hockenberry (348795)
Claus O. Wilke (188638)

November 2018

Distributions of multi-class macro F1 score for prediction of growth conditions from mRNA or protein...

The accuracy of team performance prediction based on chat log-based and, particularly, network-based features.

Victor Amelkin (5843501)
Omid Askarisichani (5843504)
Young Ji Kim (5843507)
Thomas W. Malone (675425)
Ambuj K. Singh (4503292)

October 2018

The best elastic net regression model is used for score prediction of each team on each task. On the...

Prediction performance, random forest.

Robin Gradin (6392132)
Malin Lindstedt (103918)
Henrik Johansson (519182)

February 2019

The plot shows the predictive performances for the different methods when normalized data were class...

The prediction accuracy of the baseline best regression models for each task.

Victor Amelkin (5843501)
Omid Askarisichani (5843504)
Young Ji Kim (5843507)
Thomas W. Malone (675425)
Ambuj K. Singh (4503292)

October 2018

On the displayed scatter plots, the x-coordinate corresponds to the true task score of a team, the y...

Performance metrics of the final prediction model on the test set using the random forest method.

Fei Yang (46361)
Tong Meng (462237)
Ben Torben-Nielsen (14725376)
Carsten Magnus (327831)
Chuang Liu (165506)
Emilie Dejean (14725379)

March 2023

Performance metrics of the final prediction model on the test set using the random forest method.</p

Forecasting performance comparison by RMSE, MAE and MAPE.

Yan-Ling Zheng (701006)
Li-Ping Zhang (1489732)
Xue-Liang Zhang (701007)
Kai Wang (21246)
Yu-Jian Zheng (701008)

March 2015

RMSE = root mean square error, MAE = mean absolute error and MAPE = mean absolute percentage erro...

Comparison of existing dTm prediction methods dataset statistics Q (accuracy), MAE (mean absolute error), and r2 (pearson correlation).

Kenneth N. McGuinness (5729180)
Weilan Pan (2695894)
Robert P. Sheridan (1271010)
Grant Murphy (5729183)
Alejandro Crespo (1445995)

September 2018

Comparison of existing dTm prediction methods dataset statistics Q (accuracy), MAE (mean absolute er...

Correlations between the prediction performance of the random forest models using different features and self-reported SWL.

Lushi Chen (141558)
Tao Gong (178414)
Michal Kosinski (463182)
David Stillwell (463183)
Robert L. Davidson (4591495)

November 2017

Correlations between the prediction performance of the random forest models using different featu...

Prediction efficiencies of random forest and two neighbor-assisted methods.

Juyong Lee (399709)
Jooyoung Lee (399710)

April 2013

Prediction efficiencies are measured by AUC (area under the PR curve) and the maximum value of MC...

A summary of model performance in predicting spread across the three periods.

Justin Kaashoek (14216292)
Christian Testa (10007166)
Jarvis T. Chen (9541445)
Lucas M. Stolerman (7163918)
Nancy Krieger (275361)
William P. Hanage (9553184)
Mauricio Santillana (528516)

December 2022

We choose to evaluate model performance on R2 of residuals as well as the Mean Absolute Error. We al...

Performance metrics of the prediction models using logistic regression and random forest methods with five-fold cross-validation.

Fei Yang (46361)
Tong Meng (462237)
Ben Torben-Nielsen (14725376)
Carsten Magnus (327831)
Chuang Liu (165506)
Emilie Dejean (14725379)

March 2023

Performance metrics of the prediction models using logistic regression and random forest methods wit...

The prediction performances for drug-target features and chemical fingerprint features using Elastic Net.

Alina Malyutina (6714752)
Muntasir Mamun Majumder (6714755)
Wenyu Wang (800903)
Alberto Pessia (6714758)
Caroline A. Heckman (5339465)
Jing Tang (33607)

May 2019

RMSE: root mean square error, R2: coefficient of determination, COR: Pearson correlation, MAE: mean ...

Comparison of the prediction errors between two computational models.

Seungsoo Hahn (175873)
Dongsup Kim (50568)

March 2012

The prediction errors from the MS and SS models are plotted as circles and triangles, respectivel...

Prediction performance comparison.

Qianyun Li (528993)
Runmin Shi (6407282)
Faming Liang (186676)

February 2019

Comparison of the prediction performance (measured by corr(Ytest, )) of the mixture regression model...

Root mean squared error of prediction (RMSEP) for different number of components of partial least squares (PLS) repression.

Alamgir Kabir (548332)
Md. Jahanur Rahman (4707907)
Abu Ahmed Shamim (548334)
Rolf D. W. Klemm (4707904)
Alain B. Labrique (240434)
Mahbubur Rashid (240438)
Parul Christian (548336)
Keith P. West Jr. (240443)

December 2017

Root mean squared error of prediction (RMSEP) for different number of components of partial least...

Performance of multi-class predictions.

M. Umut Caglar (5925230)
Adam J. Hockenberry (348795)
Claus O. Wilke (188638)

November 2018

Distributions of multi-class macro F1 score for prediction of growth conditions from mRNA or protein...

The accuracy of team performance prediction based on chat log-based and, particularly, network-based features.

Victor Amelkin (5843501)
Omid Askarisichani (5843504)
Young Ji Kim (5843507)
Thomas W. Malone (675425)
Ambuj K. Singh (4503292)

October 2018

The best elastic net regression model is used for score prediction of each team on each task. On the...

Prediction performance, random forest.

Robin Gradin (6392132)
Malin Lindstedt (103918)
Henrik Johansson (519182)

February 2019

The plot shows the predictive performances for the different methods when normalized data were class...

The prediction accuracy of the baseline best regression models for each task.

Victor Amelkin (5843501)
Omid Askarisichani (5843504)
Young Ji Kim (5843507)
Thomas W. Malone (675425)
Ambuj K. Singh (4503292)

October 2018

On the displayed scatter plots, the x-coordinate corresponds to the true task score of a team, the y...

Performance metrics of the final prediction model on the test set using the random forest method.

Fei Yang (46361)
Tong Meng (462237)
Ben Torben-Nielsen (14725376)
Carsten Magnus (327831)
Chuang Liu (165506)
Emilie Dejean (14725379)

March 2023

Performance metrics of the final prediction model on the test set using the random forest method.</p

Forecasting performance comparison by RMSE, MAE and MAPE.

Yan-Ling Zheng (701006)
Li-Ping Zhang (1489732)
Xue-Liang Zhang (701007)
Kai Wang (21246)
Yu-Jian Zheng (701008)

March 2015

RMSE = root mean square error, MAE = mean absolute error and MAPE = mean absolute percentage erro...

Comparison of existing dTm prediction methods dataset statistics Q (accuracy), MAE (mean absolute error), and r2 (pearson correlation).

Kenneth N. McGuinness (5729180)
Weilan Pan (2695894)
Robert P. Sheridan (1271010)
Grant Murphy (5729183)
Alejandro Crespo (1445995)

September 2018

Comparison of existing dTm prediction methods dataset statistics Q (accuracy), MAE (mean absolute er...

Correlations between the prediction performance of the random forest models using different features and self-reported SWL.

Lushi Chen (141558)
Tao Gong (178414)
Michal Kosinski (463182)
David Stillwell (463183)
Robert L. Davidson (4591495)

November 2017

Correlations between the prediction performance of the random forest models using different featu...

Prediction efficiencies of random forest and two neighbor-assisted methods.

Juyong Lee (399709)
Jooyoung Lee (399710)

April 2013

Prediction efficiencies are measured by AUC (area under the PR curve) and the maximum value of MC...

A summary of model performance in predicting spread across the three periods.

Justin Kaashoek (14216292)
Christian Testa (10007166)
Jarvis T. Chen (9541445)
Lucas M. Stolerman (7163918)
Nancy Krieger (275361)
William P. Hanage (9553184)
Mauricio Santillana (528516)

December 2022

We choose to evaluate model performance on R2 of residuals as well as the Mean Absolute Error. We al...

Performance metrics of the prediction models using logistic regression and random forest methods with five-fold cross-validation.

Fei Yang (46361)
Tong Meng (462237)
Ben Torben-Nielsen (14725376)
Carsten Magnus (327831)
Chuang Liu (165506)
Emilie Dejean (14725379)

March 2023

Performance metrics of the prediction models using logistic regression and random forest methods wit...

The prediction performance for Elastic Net, Random Forests and Support Vector Machines, as compared to the upper limit when randomly selecting one technical replicate as the prediction.

Abstract

Extracted data

The prediction performance for Elastic Net, Random Forests and Support Vector Machines, as compared to the upper limit when randomly selecting one technical replicate as the prediction.

Abstract

Extracted data

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