Most of the open-source decision tree algorithms are based on three splitting criteria (Entropy, Gini Index, and Gain Ratio). Therefore, the advantages and disadvantages of these three popular algorithms need to be studied more thoroughly. Comparisons of the three algorithms were mainly performed with respect to the predictive performance. In this work, we conducted a comparative experiment on the splitting criteria of three decision trees, focusing on their interpretability. Depth, homogeneity, coverage, lift, and stability were used as indicators for measuring interpretability. To measure the stability of decision trees, we present a measure of the stability of the root node and the stability of the dominating rules based on a measure of the similarity of trees. Based on 10 data collected from UCI and Kaggle, we compare the interpretability of DT (Decision Tree) algorithms based on three splitting criteria. The results show that the GR (Gain Ratio) branch-based DT algorithm performs well in terms of lift and homogeneity, while the GINI (Gini Index) and ENT (Entropy) branch-based DT algorithms performs well in terms of coverage. With respect to stability, considering both the similarity of the dominating rule or the similarity of the root node, the DT algorithm according to the ENT splitting criterion shows the best results.

1. 서 론
2. 연구배경
    2.1 대표적인 DT 알고리즘의 분기기준
    2.2 머신러닝 알고리즘의 해석력
    2.3 DT 알고리즘의 해석력
3. DT 알고리즘의 해석력 평가척도
    3.1 기존의 평가 척도
    3.2 해석력 관점에서 DT 알고리즘의 안정성
4. 세 개의 DT 알고리즘의 해석력 비교실험
    4.1 DT의 깊이에 따른 3가지 분기기준의 동질성,커버리지 그리고 리프트 비교
    4.2 3가지 분기기준의 안정성 비교
5. 결론 및 추후 연구방향
References

• Jung-Sik Hong(서울과학기술대학교 산업공학과) | 홍정식 Corresponding Author
• Geun-Seong Hwang(서울과학기술대학교 일반대학원 데이터사이언스학과) | 황근성