Examples of random events and the respective binary outcome. by Matheus Sant’Ana Lima (9937809)

Optimization of tracking parameters based on <i>P</i>_<i>swap</i>. by Benjamin Gallois (10131683)

ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level by Giovanni Nattino (561797)

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)

Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

Testing results for classifying AD, MCI and NC. by Nicodemus Songose Awarayi (18414494)

Summary of existing CNN models. by Nicodemus Songose Awarayi (18414494)

UK Biobank data wrangling statistics and results. by Simeone Marino (81051)

Fig 12 - by Nisha Yadav (366131)

Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

Spontaneous ERG-based random forest model discriminates control and disease cases in rodent models of obesity and type 1 diabetes and predicts disease evolution in the type 1 diabe... by Ramsés Noguez Imm (6658637)

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…”

Data_Sheet_1_The impact of family urban integration on migrant worker mental health in China.docx by Xiaotong Sun (6535064)

“…The results of this study lead the authors to recommend formulating a family-centered policy for migrant workers to reside in urban areas, optimizing the allocation of medical resources and public services, and improving family urban integration among migrant workers in order to avoid mental health problems in the process of urban integration.…”

Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx by Ali Nabavi (21097424)

“…Demographic, clinical, and heavy metal biomarker data (e.g., blood lead and cadmium levels) were analyzed as features, with hearing loss status—defined as a pure-tone average threshold exceeding 25 dB HL across 500, 1,000, 2000, and 4,000 Hz in the better ear—serving as the binary outcome. Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …”