Clarify of the Random Forest Algorithm in an Educational Field

Abstract

Many supportive decision systems using classification algorithms have been built as a black box in the last years. Such systems were hiding its inner operations to users. Lack of explanation of these algorithms leads to a practical problem. The education field is one of the areas that needs more clarification in such systems to help users in order to get more information for a right decision. In this paper, the Random Forest algorithm has been clarified and used in analyzing the students’ performance, as a dataset. The result showed that the clarified method of the aforementioned algorithm can give an accuracy of 83.56%. On the other hand, WEKA tool gives an accuracy of 80.82% for the same algorithm and dataset. Also, the proposed method of the Random Forest algorithm has been tested using another previous study’s dataset. The comparison results showed that the proposed method can give an accuracy of 92.65%, which is in turn better than the accuracy of 91.2% that obtained by another study done. Furthermore, to make the Random Forest algorithm work as a white box, Rules have been extracted from the Random Forest black box algorithm in order to make it more interpretable and helpful in predicting student’s performance.