Data and code resources. by Sam Hall-McMaster (10343795)

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Convergence curve of the DBO algorithm. by Ma Haohao (22177538)

“…The improved Dung Beetle Optimization algorithm, Back Propagation Neural Network, Finite Element Analysis, and Response Surface Methodology provide a strong guarantee for the selection of robot polishing process parameters. …”

Features selection using the Boruta algorithm. by Nishat Tasnim Thity (21755858)

“…We identified the important features related to IA using the Boruta algorithm. Predictions were made using different machine learning (ML) (decision tree (DT), random forest (RF), support vector machines (SVMs), and logistic regression (LR)) models. …”

Feature selection using Boruta algorithm. by Shayla Naznin (13014015)

“…Feature selection was performed using the Boruta algorithm and model performance was evaluated by comparing accuracy, precision, recall, F1 score, MCC, Cohen’s Kappa and AUROC.…”

Feature selection using the Boruta algorithm. by Guang Tu (22054865)

“…We extracted baseline characteristics, laboratory parameters, and clinical outcomes. The Boruta algorithm was employed for feature selection to identify variables significantly associated with in-hospital mortality, and 16 machine learning models, including logistic regression, random forest, gradient boosting, and neural networks, were developed and compared using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. …”

Code used for data analysis in this study. by Nourin Sultana (22339463)

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Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

Variable selection procedure using the Boruta algorithm. by Qiudie Liu (22655907)

“…<p>Variable selection procedure using the Boruta algorithm.</p>…”

Feature selection using the Boruta algorithm method. by Alemu Birara Zemariam (17540938)

Participant selection algorithm. by Alex Gough (16734033)

Participant selection algorithm. by Alex Gough (16734033)

The selection algorithm flowchart. by Shiyu Wang (813972)

Number of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Number of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

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F1 score of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

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F1 score of a given number of selected edges on the chain graph dataset. by Tomokaze Shiratori (9635271)

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F1 score of a given number of selected edges on the random graph dataset. by Tomokaze Shiratori (9635271)

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F1 score of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

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GA pseudo-code. by Jianpeng Zhang (528185)

“…GA is used to optimize the feature selection process to identify the key feature subsets that have the greatest impact on model performance. …”

Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

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