Examples of random events and the respective binary outcome. by Matheus Sant’Ana Lima (9937809)

Optimization of tracking parameters based on <i>P</i>_<i>swap</i>. by Benjamin Gallois (10131683)

UK Biobank data wrangling statistics and results. by Simeone Marino (81051)

ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level by Giovanni Nattino (561797)

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

SHAP bar plot. by Meng Cao (105914)

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

Sample screening flowchart. by Meng Cao (105914)

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

Descriptive statistics for variables. by Meng Cao (105914)

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

SHAP summary plot. by Meng Cao (105914)

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

ROC curves for the test set of four models. by Meng Cao (105914)

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

Display of the web prediction interface. by Meng Cao (105914)

“…Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP by Xiaoyuan Wang (492534)

The data processing and analysis pipeline. by Xin Zhang (35492)

Testing results for classifying AD, MCI and NC. by Nicodemus Songose Awarayi (18414494)

Summary of existing CNN models. by Nicodemus Songose Awarayi (18414494)

Data_Sheet_1_Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.ZIP by Xiaofeng Wang (119575)

Fig 12 - by Nisha Yadav (366131)