Categorical Variable Coding for Machine Learning in Engineering Education

This work-in-progress research paper describes a study of different categorical data coding procedures for machine learning (ML) in engineering education. Often left out of methodology sections, preprocessing steps in data analysis can have important ramifications on project outcomes. In this study, we applied three different coding schemes (i.e., scalar conversion, one-hot encoding, and binary) for the categorical variable of Race across three different ML models (i.e., Neural Network, Random Forest, and Naïve Bayes classifiers) looking at the four standard measures of ML classification models (i.e., accuracy, precision, recall, and F1-score). Results showed that in general, the coding scheme did not affect predictive outcomes as much as ML model type did. However, one-hot encoding - the strategy of transforming a categorical variable with $k$ possible values to $k$ binary nodes, a common practice in educational research - does not work well with a Naïve Bayes classifier model. Our results indicate that such sensitivity studies at the beginning of ML modeling projects are necessary. Future work includes performing a full range of sensitivity studies on our complete, grant-funded project dataset that has been collected, and publishing our findings.