CatBoost algorithm structure to predict clinical outcome. by Andrew McDonald (2307400)

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Random Forest and Gradient Boosting algorithms. by Yvan J. Garcia-Lopez (20783591)

The pseudocode of the RFE algorithm. by Zuzhen Ji (17593348)

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. …”

Example measurement and analysis units for landscape elements. by Jaewon Han (8660295)

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The steps of applying RF algorithm. by Zuzhen Ji (17593348)

Comparison of the performance of ML algorithms in predicting maize yield using 5-fold CV. by Oumnia Ennaji (20442244)

Algorithm of model development and validation. by Bethan Cracknell Daniels (20469396)

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

Ranking of ML algorithms. 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. …”

Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

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. …”

Data Sheet 1_Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis.docx by Jingtai Ma (21225476)

Types of machine learning algorithms. by Md. Merajul Islam (12646837)

“…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.</p><p>Materials and methods</p><p>This study used the latest nationally representative cross-sectional Bangladesh demographic health survey (BDHS), 2017–18 data. …”

Decision tree algorithms. by Mahbub E. Sobhani (22278967)

“…We have used Random Forest, Bagging, and Boosting (AdaBoost) algorithms and have compared their performances. …”

Feature importance comparison between the machine learning algorithms used in this study. by Sukanta Chakraborty (22693681)

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Schematic diagram of the SARIMA algorithm flow. by Yongxin Dai (13796385)

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Noise reduction by the sliding average algorithm. by Yongxin Dai (13796385)

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The goodness of fit criteria results in all algorithms for optimal hyperparameter values. by Sevtap Tırınk (22394716)