Binary classification task performance. by Thierry Jean (20691795)

Fast-and-frugal tree for trampoline intervention including predictive performance. by Lena Siebert (22075274)

Fast-and-frugal tree for volleyball intervention including the predictive performance. by Lena Siebert (22075274)

Accuracy measures of the trained models. by Sepideh Mazrouee (5191339)

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water by Kuan Huang (1504921)

“…The best regression model (<i>R</i>² = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. …”

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water by Kuan Huang (1504921)

“…The best regression model (<i>R</i>² = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. …”

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water by Kuan Huang (1504921)

“…The best regression model (<i>R</i>² = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. …”

Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke by Chulho Kim (622686)

“…We applied binary logistic regression, naïve Bayesian classification, single decision tree, and support vector machine for the binary classifiers, and we assessed performance of the algorithms by F1-measure. …”

UNet++ type architecture [68] used for all the problems in this paper. by Dylan D. Ray (3741823)

Ordinal regression task performance. by Thierry Jean (20691795)

<b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b> by BRISC Dataset (22559540)

“…<br>- All images are T1-weighted slices.</p><h2> Dataset statistics</h2><p><br></p><p dir="ltr">- Total samples: 6,000 (5,000 train / 1,000 test)<br>- Classes: 4 (balanced distribution across train/test)<br>- Planes: Axial / Coronal / Sagittal (balanced representation)<br>- Imaging modality: T1-weighted MRI<br>- Annotation quality: Reviewed and corrected by medical experts</p><h2> Citation</h2><p dir="ltr">If you use BRISC in your work, please cite:</p><p dir="ltr"><code>@article{fateh2025brisc,</code><br><code>title={Brisc: Annotated dataset for brain tumor segmentation and classification with swin-hafnet},</code><br><code>author={Fateh, Amirreza and Rezvani, Yasin and Moayedi, Sara and Rezvani, Sadjad and Fateh, Fatemeh and Fateh, Mansoor and Abolghasemi, Vahid},</code><br><code>journal={arXiv preprint arXiv:2506.14318},</code><br><code>year={2025}</code><br><code>}</code></p><h2> Acknowledgments</h2><p dir="ltr">Thanks to the collaborating radiologists and physicians for expert annotation and review.…”

Analysis of geo-spatiotemporal data using machine learning algorithms and reliability enhancement for urbanization decision support by Kwame O. Hackman (9289505)

Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports by Olivier Q. Groot (9370461)

“…In the independent test set (<i>n</i> = 141) not used for model development, the NLP algorithm achieved an 0.97 AUC-ROC (95% confidence interval [CI], 0.92–0.99) for classification of multiple bone metastases and an 0.99 AUC-PRC (95% CI, 0.99–0.99). …”

Image_1_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.JPEG by Kirsty E. Waddington (5754545)

“…Lasso logistic regressions had the greatest proportion of correct classifications [F1 score (accuracy measure) = 0.808, specificity = 0.913]. …”

Image1_Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease.eps by Ryszard Kubinski (12105983)

“…We collected fifteen 16S rRNA microbiome datasets (7,707 samples) from North America to benchmark combinations of gut microbiome features, data normalization and transformation methods, batch effect correction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with two binary classification metrics following leave-one-dataset-out cross (LODO) validation, where all samples from one study were left out of the training set and tested upon. …”

Data_Sheet_1_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.xlsx by Kirsty E. Waddington (5754545)

“…Lasso logistic regressions had the greatest proportion of correct classifications [F1 score (accuracy measure) = 0.808, specificity = 0.913]. …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…The single predictor variable was the mushroom habitat, a categorical feature that was preprocessed using the One-Hot Encoding technique, resulting in seven distinct binary variables. …”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”

The Value of Dynamic Grip Force Modulation as a Potential Biomarkerfor Hand Function Recovery Following Stroke by Kirstin-Friederike Heise (7518953)

Data_Sheet_3_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.xlsx by Kirsty E. Waddington (5754545)

“…Lasso logistic regressions had the greatest proportion of correct classifications [F1 score (accuracy measure) = 0.808, specificity = 0.913]. …”