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Prediction accuracy analysis with logistic regression and CART decision tree
Author(s): Xudong Zhang; Di Wang; Ying Qian; Yingming Yang
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Paper 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.

Paper Details

Date Published: 31 July 2019
PDF: 7 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 1119810 (31 July 2019); doi: 10.1117/12.2540361
Show Author Affiliations
Xudong Zhang, State Grid Zhejiang Electric Power Co., Ltd. (China)
Di Wang, State Grid Energy Research Institute Co., Ltd. (China)
Ying Qian, East China Normal Univ. (China)
Yingming Yang, East China Normal Univ. (China)


Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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