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Tree-based machine learning methods for modeling and forecasting mortality

Recurso electrónico / Electronic resource
Registro MARC
Tag12Valor
LDR  00000cab a2200000 4500
001  MAP20220026109
003  MAP
005  20221003155655.0
008  221003e20220905bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
1001 ‎$0‎MAPA20220008556‎$a‎Skovgaard Bjerre, Dorethe
24510‎$a‎Tree-based machine learning methods for modeling and forecasting mortality‎$c‎Dorethe Skovgaard Bjerre
520  ‎$a‎Machine learning has recently entered the mortality literature in order to improve the forecasts of stochastic mortality models. This paper proposes to use two pure, tree-based machine learning models: random forests and gradient boosting, based on the differenced log-mortality rates to produce more accurate mortality forecasts. These forecasts are compared with forecasts from traditional, stochastic mortality models and with forecasts from random forests and gradient boosting variants of the stochastic models. The comparisons are based on the Model Confidence Set procedure. The results show that the pure, tree-based models significantly outperform all other models in the majority of cases considered. To address the lack of interpretability issue associated with machine learning models, we demonstrate how to extract information about the relationships uncovered by the tree-based models. For this purpose, we consider variable importance, partial dependence plots, and variable split conditions. Results from the in-sample fit suggest that tree-based models can be very useful tools for detecting patterns within and between variables that are not commonly identifiable with traditional methods.
650 4‎$0‎MAPA20170005476‎$a‎Machine learning
650 4‎$0‎MAPA20080555306‎$a‎Mortalidad
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
7730 ‎$w‎MAP20077000420‎$g‎05/09/2022 Volumen 52 Número 3 - septiembre 2022 , p. 765-787‎$x‎0515-0361‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association