Naive Bayes

Abstract

Dr. Shonn Cheng recently conducted an R U Serious session for META Lab members via Teams, focusing on Naive Bayes in monkey learning. The session introduced the Naive Bayes classifier, explaining its probabilistic foundation and the concept of conditional independence between features. Participants explored key applications of Naive Bayes in classification tasks, particularly in text classification, such as spam detection. Dr. Cheng provided hands-on demonstrations in R, guiding participants through implementing the Naive Bayes algorithm, tuning model parameters, and evaluating performance. The session emphasized the importance of understanding the underlying assumptions of Naive Bayes and selecting appropriate models based on data characteristics, offering participants valuable insights for applying Naive Bayes in their research.

Shonn Cheng

Assistant Professor

My research interests include applying a variety of analytical methods to study motivation, expertise, and technology-enhanced training.