Predictive Modeling Applications in Actuarial Science
- Volume 1
- Introduction
- Predictive Modeling Foundations
- Predictive Modeling Methods
- Bayesian and Mixed Modeling
- Longitudinal Modeling
- Volume 2
- Generalized Linear Model
- Extensions of the Generalized Linear Model
- Unsupervised Predictive Modeling Methods
-
Applications on Current Problems in Actuarial Science
- Chapter 8 - The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
- Chapter 9 - Finite Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
- Chapter 10 - A Framework for Managing Claim Escalation Using Predictive Modeling
- Chapter 11 - Predictive Modeling for Usage-Based Auto Insurance
Chapter 12 - Unsupervised Learning
Authors
Louise Francis | Francis Analytics and Actuarial Data Mining, Inc
louise.francis@data-mines.com
Chapter Preview
The focus of this chapter will be on various methods of unsupervised learning. Unsupervised learning will be contrasted with supervised learning and the role of unsupervised learning in a supervised analysis will also be discussed. The concept of dimension reduction will first be presented. We will then introduce the common methods of dimension reduction, principal components/factor analysis and clustering. More recent developments on the classic techniques such fuzzy clustering will also be introduced. Illustrative examples will be presented that use publicly available databases. At the end of the chapter we will provide exercises that use data supplied with the chapter.
| Data | R Code |
| Indices | |
| Indices 2 | |
| SimClus_Fraud1 | |
| SimClus_Ques.claimsData | |
| SimClusBI_out1 | |
| Cluster Code for Book | |
| PrinComp R Code |