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Statistical Learning
| Content Provider | Scilit |
|---|---|
| Author | Charpentier, Arthur |
| Copyright Year | 2014 |
| Description | In this chapter, we will describe some techniques to learn from data, and to make a prediction based on a set of features. We will use a training set, where those features were observed, as where the variable of interest might be • Whether an insured will buy additional (optional) coverage, or not • Whether a claimant will be represented by an attorney, or not (see e.g. the automobile injury insurance claims in Frees (2009)) • Whether an insured will have some specific disease, or not • Whether a loaner will be considered a good or a bad client (in this chapter) All the techniques mentioned in this chapter will be used on a binary variable of interest (good or bad client), but one can easily extend most of them to an ordered discrete variable of interest. Book Name: Computational Actuarial Science with R |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2012-0-15842-1&isbn=9780429168956&doi=10.1201/b17230-10&format=pdf |
| Ending Page | 238 |
| Page Count | 42 |
| Starting Page | 197 |
| DOI | 10.1201/b17230-10 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2014-08-26 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Computational Actuarial Science with R History and Philosophy of Science Coverage Automobile Client Easily Attorney Loaner |
| Content Type | Text |
| Resource Type | Chapter |
