RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . حسب Jakub Stoklosa (3315042)

A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets usi... حسب Olaide N. Oyelade (14047002)

A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional data... حسب Olaide N. Oyelade (14047002)

A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datas... حسب Olaide N. Oyelade (14047002)

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

"…However, ToxCast assays differ in the amount of data and degree of class imbalance (CI). Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …"

Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. حسب Ramsés Noguez Imm (6658637)

Latent state estimations for experimental data with no stressors. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 18. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 22. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 17. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 13. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 3. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 15. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 7. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 1. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 15. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 21. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 5. حسب Samiul Alam (18563323)