Binary classification task performance. by Thierry Jean (20691795)

Individual Transition Label Noise Logistic Regression in Binary Classification for Incorrectly Labeled Data by Seokho Lee (10088)

“…<p>We consider a binary classification problem in the case where some observations in the training data are incorrectly labeled. …”

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. …”

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

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)

Ordinal regression task performance. by Thierry Jean (20691795)

Statistics in Proteomics: A Meta-analysis of 100 Proteomics Papers Published in 2019 by David C. L. Handler (8791451)

“…This included questions such as whether a pilot study was conducted and whether false discovery rate calculation was employed at either the quantitation or identification stage. These data were then transformed to binary inputs, analyzed via machine learning algorithms, and classified accordingly, with the aim of determining if clusters of data existed for specific journals or if certain statistical measures correlated with each other. …”

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

Variable Selection and Estimation for Misclassified Binary Responses and Multivariate Error-Prone Predictors by Li-Pang Chen (9747423)

“…<p>In statistical analysis or supervised learning, classification has been an attractive topic. Typically, a main goal is to adopt predictors to characterize the primarily interested binary random variables. …”

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

“…Two classification algorithms – random forest (RF) and support vector machines (SVM) – were used to produce binary (built-up / non-built up) maps for all years within the temporal span. …”

Baseline sociodemographic and clinical data by Kexin Qu (10285073)

“…Performance was evaluated on models developed on the training data, on the same models applied to an external test set and through internal validation with three bootstrap algorithms to correct for overoptimism. …”

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

“…The aim of this study was to develop a natural language processing (NLP) algorithm for binary classification (single metastasis versus two or more metastases) in bone scintigraphy reports of patients undergoing surgery for bone metastases.…”

Candidate predictors by Kexin Qu (10285073)

“…Performance was evaluated on models developed on the training data, on the same models applied to an external test set and through internal validation with three bootstrap algorithms to correct for overoptimism. …”

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). …”

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. …”

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]. …”

Data_Sheet_2_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]. …”

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]. …”

Data_Sheet_4_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.pdf by Kirsty E. Waddington (5754545)

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