Let’s say there are 100 records in the training dataset. The observations are arranged in decreasing order of probability ...

Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

Abstract This study presents a comparative analysis of machine learning models for threat detection in Internet of Things (IoT) devices using the CICIoT2023 dataset. We evaluate Logistic Regression, K ...

This research paper examines the accuracy of the logistic regression method in predicting employee attrition. Employee attrition is of particular interest to human resources departments due to its ...

Article citations More>> Kirasich, K., Smith, T. and Sadler, B. (2018) Random Forest vs Logistic Regression: Binary Classification for Heterogeneous Datasets. SMU Data Science Review, 1, Article 9.

Establishing binary logistic regression allowed researchers to study the marginal impacts of the predictor variables on the phenomenon studied (i.e., ORs) and propose the SWICS-30 score.

For binary control variables, we use the phi correlation, while for non-binary control variables, we employ the point-biserial correlation coefficient. Our results reveal a positive correlation ...

Simulations conducted on datasets from the German Breast Cancer Study Group (GBSG), the Surveillance, Epidemiology, and End Results Program (SEER), and the Wisconsin Diagnostic Breast Cancer (WDBC) ...