Flow chart of edge folding algorithm. by Cuiying Zhou (10048078)

The schematic diagram of the hierarchical clustering algorithm. by Xiaojie Feng (6873953)

Subjects: “…original multidimensional data…”

The silhouette coefficient scores for different algorithms at various levels. by Xiaojie Feng (6873953)

Subjects: “…original multidimensional data…”

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

“…</p><p>Methods</p><p>Multiple machine learning (ML) algorithms were applied to data from the 2022 Bangladesh Demographic Health Survey, including Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, XGBoost, LightGBM and Neural Networks. …”

A schematic diagram of the settlement spatial distribution model based on factor analysis integrated with the hierarchical clustering algorithm. by Xiaojie Feng (6873953)

Subjects: “…original multidimensional data…”

RFEcv algorithm flowchart. by Xinrui Wang (488330)

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

Data types and sources. by Xiaojie Feng (6873953)

Subjects: “…original multidimensional data…”

Variables tested in the ML algorithms. by Gilson Yuuji Shimizu (19837946)

“…Data from Beth Israel Deaconess Medical Center (BIDMC), USA, were used for external validation. …”

bppMigration-algorithms-data.tgz by Ziheng Yang (22169779)

“…<p dir="ltr">Population phylogenomics uses sampled genomes to jointly infer population genetic processes (ancestral and contemporary population sizes, historical gene flow) and a phylogenetic tree relating species or populations including species divergence times. …”

The accuracy score change matrix following model development. by Uğur Ejder (22683228)

Subjects:

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. …”

Receiver operator characteristic (ROC) curves of the machine learning algorithms. by Yue Lv (549862)

Comparison of Proposed Model using Unseen Data. by Saim Chishti (22272188)

“…To effectively analyze more complex medical data, more robust machine learning models have been developed to address various health issues. …”

Comparison of proposed model using unseen data. by Saim Chishti (22272188)

“…To effectively analyze more complex medical data, more robust machine learning models have been developed to address various health issues. …”

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)

“…This study focuses on developing an efficient classification framework for species-level tree mapping in the Hauz Khas Urban Forest, New Delhi, India, using EO-1 Hyperion hyperspectral imagery.…”