Performance metrics for random forest binary classification models. by Alex P. Lind (6945287)

Random forest binary classification of the colorized Corona image for the RSK region, Morocco (1972). by Safia Loulad (16913691)

Model 1: All Variables for binary classification. by Toheeb Salahudeen (21368040)

“…Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. …”

Information Value Scores (Binary Logistic Regression) and Feature Importance Scores (Random Forest) Across Features (for IVs from LR, a bar with dashed border indicates a significa... by Anis Ben Ghorbal (22521828)

Random Forest Framework. by Anis Ben Ghorbal (22521828)

Random forest algorithm: Method and example results. by Natalie Weed (7345124)

“…Note the pronounced variation in density along the a-p axis. (<b>C</b>) Binary mask for primary somatosensory cortex barrel field (SSp-bfd). …”

Supplementary Figure 4 from A Novel Sensitivity Maximization at a Given Specificity Method for Binary Classifications by Seyyed Mahmood Ghasemi (20815345)

Class distribution for binary classes. by Toheeb Salahudeen (21368040)

“…Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. …”

Data Sheet 1_Bundled assessment to replace on-road test on driving function in stroke patients: a binary classification model via random forest.docx by Lu Huang (211625)

“…The subject was classified as either Success or Unsuccess group according to whether they had completed the on-road test. A random forest algorithm was then applied to construct a binary classification model based on the data obtained from the two groups.…”

Identification of genetic markers for cortical areas using a Random Forest classification routine and the Allen Mouse Brain Atlas by Natalie Weed (7345124)

“…To screen for genes that change expression at area borders, we employed a random forest algorithm and binary region classification. …”

Binary Database of Patagonian Archaeological Technology from Hunter–gatherer mobility and technological landscapes in southernmost South America: a statistical learning approach. by Ivan Briz i Godino (719098)

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

“…In addition, hyperparameter tuning of the RF algorithm significantly improved F1 on CI assays. This study provided a basis for developing a toxicity classification model with improved performance by evaluating the effects of data set characteristics. …”

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

“…Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. …”

Table 1_A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing... by Charmayne Mary Lee Hughes (12959972)

“…</p>Conclusion<p>Machine learning algorithms show promise for the detection of current frailty status, particularly in binary classification. …”

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)

“…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”

Creation of top projections from coronal images of gene expression. by Natalie Weed (7345124)

Telehealth. by Anis Ben Ghorbal (22521828)

“…The presence of perceived barriers also had a positive effect (OR=3.62, p = 0.024). The random forest algorithm outperformed logistic regression in terms of classification accuracy and AUC (0.774 versus 0.758 in the test set). …”

Kernel Density Plot of Effort Expectancy Scores. by Anis Ben Ghorbal (22521828)

“…The presence of perceived barriers also had a positive effect (OR=3.62, p = 0.024). The random forest algorithm outperformed logistic regression in terms of classification accuracy and AUC (0.774 versus 0.758 in the test set). …”

Comparing the performance of One-class classifiers with their respective binary classifier. by Efrat Cohen-Davidi (19485986)

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)