Prediction accuracy analysis with logistic regression and CART decision tree

Xudong Zhang; Di Wang; Ying Qian; Yingming Yang

doi:10.1117/12.2540361

31 July 2019 Prediction accuracy analysis with logistic regression and CART decision tree

Xudong Zhang, Di Wang, Ying Qian, Yingming Yang

Proceedings Volume 11198, Fourth International Workshop on Pattern Recognition; 1119810 (2019) https://doi.org/10.1117/12.2540361
Event: Fourth International Workshop on Pattern Recognition, 2019, Nanjing, China

Abstract

Classification is one of the most important techniques in machine learning. In classification problems, logistic regression and decision tree are two efficient algorithms in supervised learning. In this paper, we tested logical regression and CART decision tree algorithms on different datasets. The results received from experiments showed that CART decision tree performs much better in data set with more attributes and slight imbalanced data distribution. At the same time logistic regression is more accurate on datasets with fewer attributes and balanced data distribution.

Citation Download Citation

Xudong Zhang, Di Wang, Ying Qian, and Yingming Yang "Prediction accuracy analysis with logistic regression and CART decision tree", Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 1119810 (31 July 2019); https://doi.org/10.1117/12.2540361

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