Machine Learning with R: Logistic Regression
Interested in learning how logistic regression can be used to predict outcomes and uncover relationships in data? Logistic regression is one of the most widely used statistical and machine learning techniques for modeling binary outcomes and supporting evidence-based decision-making. Through live demonstrations and guided exercises in R, participants will gain practical experience building, evaluating, and interpreting logistic regression models.
This hands-on workshop introduces the complete logistic regression workflow, from data preparation and model development to performance assessment and result interpretation. Whether you are new to predictive modeling or looking to strengthen your analytical skills, this session will provide a practical foundation for applying logistic regression in research and data analysis.
By the end of this workshop, participants will be able to:
- Prepare and preprocess data for logistic regression modeling in R.
- Develop and train logistic regression models for binary classification tasks.
- Evaluate model performance using discrimination metrics, including the C-statistic (AUC) and ROC curves, as well as calibration measures.
- Interpret model coefficients, predicted probabilities, and the relative importance of predictor variables.
- Communicate model results and key findings effectively to research and non-technical audiences.
This session is ideal for researchers, students, and analysts interested in applying predictive modeling techniques to their data. Basic familiarity with R and statistical concepts is recommended.
Workshop Preparation
None
Facilitator Bio
Shubrandu (he/him) is a PhD Candidate in Health Research Methodology (Clinical Epidemiology) at McMaster University with a background in epidemiology and biostatistics. He has extensive experience working with large healthcare datasets and supports researchers in study design, statistical analysis, and data management. His expertise includes regression modeling, survival analysis, and handling administrative health data, with proficiency in R and SAS.
Workshop Slides
Coming soon.