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

Subjects:

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

Subjects:

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

Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

ML algorithms used in this study. by Toheeb Salahudeen (21368040)

Improved random forest algorithm. by Zhen Zhao (159931)

“…Compared with MDA-RF, the prediction accuracy of the improved RF built on the same subset increased by 1.7%, indicating that improving the bootstrap sampling of random forest by using the K-means++ clustering algorithm can enhance model accuracy to some extent. …”

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

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

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

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.…”

Pseudocode for the missForestPredict algorithm. by Elena Albu (15181070)

“…The algorithm iteratively imputes variables using random forests until a convergence criterion, unified for continuous and categorical variables, is met. …”

The schematic diagram of the iForest algorithm. by Zhibo Xie (6790775)

“…Subsampling and cross factor are designed and used to overcome the shortcomings of the isolated forest algorithm (iForest). …”

Schematic diagram of random forest algorithm. by Imee V. Necesito (12749885)

The forest plot of Preferred Terms for four TNF-α inhibitors obtained using the ROR algorithm. by Qinhui Tang (21989425)

TreeMap 2016 Forest Type Algorithm (Image Service) by U.S. Forest Service (17476914)

“…We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). …”

Hyperparameters of machine learning algorithms used grid search. by Mahade Hasan (20536430)

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. …”

Description of the dataset used for the machine learning algorithm. by Tri Ngo Quang (20390189)

Image 9_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Table 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx by Junfei Zhang (7547975)

“…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”