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)

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.…”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”