ABSTRACT
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.
