The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

Random forest with hyperparameter optimization. by Balraj Preet Kaur (20370832)

Best optimizer results for random forest. by Balraj Preet Kaur (20370832)

Best optimizer results of Random Forest. by Balraj Preet Kaur (20370832)

Results of Random Forest. by Balraj Preet Kaur (20370832)

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)

“…Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”

QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm by Z.Y. Algamal (5547620)

“…Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”

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

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

“…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

SHAP bar plot. by Meng Cao (105914)

“…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …”

Sample screening flowchart. by Meng Cao (105914)

“…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …”

Descriptive statistics for variables. by Meng Cao (105914)

“…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …”

SHAP summary plot. by Meng Cao (105914)

“…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …”

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

“…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …”

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

“…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …”

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

“…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”

Spontaneous ERG-based random forest model discriminates control and disease cases in rodent models of obesity and type 1 diabetes and predicts disease evolution in the type 1 diabe... by Ramsés Noguez Imm (6658637)