The overview of the ML algorithms’ flowchart. by Yasemin Ayaz Atalan (21989402)

“…For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. …”

Evaluation metrics of the models constructed by each algorithm. by Xiaoyan Yang (259084)

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Parallel HSMM algorithm structure. by Andrew McDonald (2307400)

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Data from the article titled Machine Learning Algorithms to Predict Digital Competencies in University Faculty by Jenniffer Sobeida Moreira Choez (16933539)

“…<p dir="ltr">The data analyzed are part of the study entitled <i>Machine Learning Algorithms to Predict Digital Competencies in University Faculty</i>, which aimed to develop predictive models based on machine learning algorithms to estimate the level of digital competencies among university faculty. …”

Hyperparameters’ setting for PSO algorithm. by Feng Shen (151470)

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Data Sheet 1_IGSA-SAC: a novel approach for intrusion detection using improved gravitational search algorithm and soft actor-critic.docx by Lizhong Jin (20991293)

Data Sheet 3_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 2_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 4_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 6_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.docx by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 1_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.pdf by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Visualization of mean absolute SHAP values for conditional algorithm for each class. by Sadia Nazim (21440115)

Visualization of mean absolute SHAP values for Interventional algorithm for each class. by Sadia Nazim (21440115)

Data Sheet 1_Efficient tree species classification using machine and deep learning algorithms based on UAV-LiDAR data in North China.docx by Hanyu Zhang (1425250)

“…Introduction<p>The unmanned aerial vehicle -based light detection and ranging (UAV-LiDAR) can quickly acquire the three-dimensional information of large areas of vegetation, and has been widely used in tree species classification.</p>Methods<p>UAV-LiDAR point clouds of Populus alba, Populus simonii, Pinus sylvestris, and Pinus tabuliformis from 12 sample plots, 2,622 tree in total, were obtained in North China, training and testing sets were constructed through data pre-processing, individual tree segmentation, feature extraction, Non-uniform Grid and Farther Point Sampling (NGFPS), and then four tree species were classified efficiently by two machine learning algorithms and two deep learning algorithms.…”

Performance comparison of ML algorithms across multiple evaluation metrics. by Muhammad Tausif (12385892)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Data_Sheet_1_Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.pdf by Mikhail Ya Yadgarov (18543769)

Comparison between three machine learning models mean accuracy score among several Raman preprocessing algorithms. by Tri Ngo Quang (20390189)

Optimal hyperparameters of the model-agnostic meta-learning algorithm with few-shot tasks. by Soraya Khanmohmmadi (21539953)