A user guide of CART and random forests with applications in FinTech and InsurTech-Reference-Cited by-同舟云学术

A user guide of CART and random forests with applications in FinTech and InsurTech

Published:2024-08-19 Issue: Volume: Page:
ISSN:2520-8756
Container-title:Japanese Journal of Statistics and Data Science
language:en
Short-container-title:Jpn J Stat Data Sci

Author:

Chen Yongzhao,Cheung Ka Chun,Sun Ross Zhengyao,Yam Sheung Chi Phillip

Abstract

AbstractIn the realm of financial data analytics, machine learning techniques, particularly classification and regression trees (CARTs) and random forests, have shown remarkable efficiency. This article serves as a user guide for these methods, with an emphasis on their applicability and effectiveness in analyzing datasets in FinTech and InsurTech. In particular, we present several numerical examples and empirical studies, and demonstrate their superiority in handling data with a variety of input features, offering insights into their potential applications in the industries.

Funder

Hang Seng University of Hong Kong

University Grants Committee

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42081-024-00258-x.pdf

Reference30 articles.

1. Agrawal, R., Mehta, M., Shafer, J. C., Srikant, R., Arning, A., & Bollinger, T. (1996). The Quest Data Mining System. KDD, 96, 244–249.

2. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

3. Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and Regression Trees. Florida, United States: CRC Press.

4. Chakraborty, A., & Kar, A. K. (2017). Swarm intelligence: A review of algorithms (pp. 475–494). Nature-inspired computing and optimization: Theory and applications.

5. Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory (2nd ed.). New York: John Wiley & Sons.