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.…”

Description of the dataset features. by Wan-Hua Zhang (21601139)

Comparison of the top 10 most important features. by Mengjiao Yin (10995604)

Policy intervention descriptions. by Zachary J. Ward (10126662)

“…We estimated the means of other maternal indicators for each group, as well as the mean impact of different policy interventions. We identified 7 groups (A-G) of country typologies with different salient features. …”

Feature frequency distribution histogram. by Tingting Tong (11993299)

“…To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. …”

Data Sheet 1_Predictive model establishment for forward-head posture disorder in primary-school-aged children based on multiple machine learning algorithms.csv by Hongjun Tao (21448853)

“…</p>Objective<p>This study aims to identify highly sensitive predictive indicators for forward head posture in primary school children using the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm. Multiple machine learning algorithms are applied to construct distinct risk prediction models, with the most effective model selected through comparative analysis. …”

CBAM module architecture. by Buyu Wang (17889178)

Study workflow diagram. by Alemu Birara Zemariam (17540938)

“…The synthetic minority oversampling technique was used for data balancing and Boruta algorithm was used to identify best features. Association rule mining using an Apriori algorithm was employed to generate the best rule for the association between the independent feature and the targeted feature using R software.…”

Stunting final dataset. by Alemu Birara Zemariam (17540938)

“…The synthetic minority oversampling technique was used for data balancing and Boruta algorithm was used to identify best features. Association rule mining using an Apriori algorithm was employed to generate the best rule for the association between the independent feature and the targeted feature using R software.…”

A mean SHAP value report. by Alemu Birara Zemariam (17540938)

“…The synthetic minority oversampling technique was used for data balancing and Boruta algorithm was used to identify best features. Association rule mining using an Apriori algorithm was employed to generate the best rule for the association between the independent feature and the targeted feature using R software.…”

A waterfall plot analysis. by Alemu Birara Zemariam (17540938)

“…The synthetic minority oversampling technique was used for data balancing and Boruta algorithm was used to identify best features. Association rule mining using an Apriori algorithm was employed to generate the best rule for the association between the independent feature and the targeted feature using R software.…”

Countplot for categorical features. by Mst. Rokeya Khatun (22226685)

“…Initially, descriptive and inferential statistical analyses were applied to identify significant predictors and guide feature selection. The hybrid feature selection strategy, combining statistical significance and model-based importance measures, revealed key features. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. …”

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

“…Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. …”