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

Comparison of prediction results from multiple models. by Wenguang Li (6528113)

Comparison chart of ROC curves for multiple models. by Wenguang Li (6528113)

Prediction to belong to Cluster 1 or Cluster 2 using random forest algorithm. by Olivier Mascle (14164997)

AUC scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

“…This strategy is integrated into a random forest algorithm by replacing the conventional voting method. …”

Recall scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

“…This strategy is integrated into a random forest algorithm by replacing the conventional voting method. …”

F1-scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

“…This strategy is integrated into a random forest algorithm by replacing the conventional voting method. …”

Study site map highlighting the 505 forest stands that were incorporated into the Habplan harvest scheduler. by Max D. Jones (6269006)

Data used in this study. by Qinghua Li (398885)

“…By comparing multiple models and validating the data with an airborne LiDAR reference dataset, the results show that the R² (R-Square) of the CNN-LightGBM model improves by more than 0.05 compared to the other models, and performs better in the experiments. …”

Flow diagram of forest management decision-making process using Habplan and <i>HabplanR</i>. by Max D. Jones (6269006)

DEM error verified by airborne data. by Qinghua Li (398885)

“…By comparing multiple models and validating the data with an airborne LiDAR reference dataset, the results show that the R² (R-Square) of the CNN-LightGBM model improves by more than 0.05 compared to the other models, and performs better in the experiments. …”

Error of ICESat-2 with respect to airborne data. by Qinghua Li (398885)

“…By comparing multiple models and validating the data with an airborne LiDAR reference dataset, the results show that the R² (R-Square) of the CNN-LightGBM model improves by more than 0.05 compared to the other models, and performs better in the experiments. …”

A new approach in soil organic carbon estimation using machine learning algorithms: a study in a tropical forest in Vietnam by Thuy Phuong Nguyen (11769999)

“…This study aimed to evaluate the ability of SOC estimation using a multiple linear regression model (MLR) and four machine learning algorithms: artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) with satellite data sources and soil nutrient indicator data to find the optimal method. …”

Details of ICESat-2 used in the training data in this study. by Qinghua Li (398885)

Histogram comparing DMFDEM, FABDEM, EU DEM against the validation data. by Qinghua Li (398885)

Confusion Matrix for the Hybrid algorithms. by Faten Al-hussein (20707521)

“…This study aims to develop hybrid prediction models that integrate the strengths of multiple algorithms to enhance HbA1c prediction accuracy while minimising the number of significant Key Performance Indicators (KPIs). …”

Experimental results after data augmentation using ADASYN. by GaoXiang Zhao (21499525)

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. …”

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)

“…Among the 6 predictive models, the random forest algorithm demonstrated the highest performance (AUC = 0.865), significantly outperforming the others. …”

Global and local feature (predictor variable) explanations from the XGBoost trained with Quebec data (2014–2021). by Hamid Ghanbari (6821096)