Fig. 3: The designed Random Forest model for classifying poor and better-performing students given a set of 71 variables. | Humanities and Social Sciences Communications

Fig. 3: The designed Random Forest model for classifying poor and better-performing students given a set of 71 variables.

From: Profiling low-proficiency science students in the Philippines using machine learning

Fig. 3

It is composed of n = 500 decision trees called estimators with a maximum tree depth of 20. Each input to the estimator uses only a subset of variables equal to ceil(log271) or 7 variables. This minimizes the model overfitting due to the original large number of variables.

Back to article page