Random Forest as a Predictive Analytics Alternative to Regression in Institutional Research
Lingjun He, Levine, R. A., Juanjuan Fan, Beemer, J., & Stronach, J. (2018). Random forest as a predictive analytics alternative to regression in institutional research. Practical Assessment, Research & Evaluation, 23(1), 1–16.
This resource highlights the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making and stresses the success of random forest in circumstances where the regression assumptions are often violated in big data applications.