Performance of machine learning algorithms. by Wenxue Liu (6877754)

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

General model performance for different algorithms. by Binghan Xue (21253235)

Subjects:

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. …”

The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…We find evidence that the generalised GLS-KGR algorithm is well-suited to such time-series applications, outperforming several standard techniques on this dataset.…”

RFEcv algorithm flowchart. by Xinrui Wang (488330)

Key Findings and Methodologies. by Carlos M. Ardila (19996060)

Subjects:

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

“…</p><p>Conclusions</p><p>Among the machine learning algorithms evaluated, Random Forest showed the best generalization ability, both internally and externally. …”

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

Learning curves under various machine algorithms. by Jinlin Teng (17972595)

Subjects:

ROC curves under various machine algorithms. by Jinlin Teng (17972595)

Subjects:

Genome-wide identification of candidate regions associated with birth weight in Lori-Bakhtiari sheep using Random Forest algorithm by Mohammad Hossein Moradi (11400671)

“…This study was conducted to identify genetic loci associated with birth weight in a meat-type sheep using a Random Forest (RF) algorithm applied to genomic data. A total of 132 Lori-Bakhtiari sheep were selected based on breeding values (EBVs) for body weight. …”

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

The comparison of results from the proposed architecture and state-of-the-art algorithms on the UNSW-B15, NSL-KDD and KDDCUP’99 dataset. by Abdullah Asım Yılmaz (20715311)

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

“…Missing data in input variables often occur at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction settings. …”

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. …”

Image 10_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. …”

Image 3_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. …”

Image 4_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. …”