MSE for ILSTM algorithm in binary classification. by Asmaa Ahmed Awad (16726315)

“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”

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

“…The model further showed superior results on binary classification compared with existing methods. …”

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

“…The model further showed superior results on binary classification compared with existing methods. …”

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)

“…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. …”

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

Urgent versus nonurgent binary classification performance. by Kyung Won Kwon (18150416)

Comparison between ILSTM and machine learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

Comparison between ILSTM and deep learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

Class distribution for binary classes. by Toheeb Salahudeen (21368040)

“…RF achieved an average accuracy of 92.7% and an F1 score of 83.95% for binary classification, 90.36% and 90.1%, respectively, for the classification of three classes of severity of depression and 89.76% and 88.26%, respectively, for the classification of five classes. …”

Binary classification predictions. by Thierry Jean (20691795)

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

“…The model further showed superior results on binary classification compared with existing methods. …”

The performance of the machine learning algorithms to differentiate between W and other sleep stages during three and five class binary classification tasks on the test set sleep s... by Sowmya M. Ramaswamy (18954755)

Binary contingency table. by Lu Xiao (314030)

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification by Vaibhavi Patel (19843282)

Binary classification task performance. by Thierry Jean (20691795)

Comparing the performance of One-class classifiers with their respective binary classifier. by Efrat Cohen-Davidi (19485986)

(a) Accuracy and (b) loss curves of training and validation during binary class classification for 512 features extracted by the CNN. by Md. Sabbir Hossain (9958939)

Model 1: All Variables for binary classification. by Toheeb Salahudeen (21368040)

“…RF achieved an average accuracy of 92.7% and an F1 score of 83.95% for binary classification, 90.36% and 90.1%, respectively, for the classification of three classes of severity of depression and 89.76% and 88.26%, respectively, for the classification of five classes. …”

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

Subjects:

Confusion matrix for a binary outcome (adapted from Fawcett [30]). by João Albuquerque (12936572)