Titlebook: Effective Statistical Learning Methods for Actuaries II; Tree-Based Methods a Michel Denuit,Donatien Hainaut,Julien Trufin Textbook 2020 Sp

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Performance Evaluation,In actuarial pricing, the objective is to evaluate the pure premium as accurately as possible. The target is thus the conditional expectation . of the response . (claim number or claim amount for instance) given the available information ..

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Friction

Bagging Trees and Random Forests,Two ensemble methods are considered in this chapter, namely bagging trees and random forests. One issue with regression trees is their high variance. There is a high variability of the prediction . over the trees trained from all possible training sets .. Bagging trees and random forests aim to reduce the variance without too much altering bias.

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Boosting Trees,Bagging trees and random forests base their predictions on an ensemble of trees. In this chapter, we consider another training procedure based on an ensemble of trees, called boosting trees. However, the way the trees are produced and combined differ between random forests (and so bagging trees) and boosting trees.

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2523-3262 ree-based methods.Fills a gap in the literature on artificia.This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based mode

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,Zur Elektrodynamik bewegter K?rper, of the contract, without loss nor profit. Under the conditions of validity of the law of large numbers, the pure premium is the expected amount of compensation to be paid by the insurer (sometimes discounted to policy issue in case of long-term liabilities).

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