Out with the opaque
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20260004112 | ||
| 003 | MAP | ||
| 005 | 20260213093301.0 | ||
| 008 | 260212e20260202gbr|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 245 | 0 | 0 | $aOut with the opaque$cKarol Gawluwski...[et al.] |
| 520 | $aThe article reviews how actuarial predictive models are evolving, comparing traditional GLM and GAM approaches with newer, more interpretable techniques such as IBLM and EBM. These modern models enhance predictive accuracy while preserving transparency, demonstrated through examples using real motor insurance data. The text highlights the growing importance of data science in actuarial practice | ||
| 650 | 4 | $0MAPA20080592059$aModelos predictivos | |
| 650 | 4 | $0MAPA20170005476$aMachine learning | |
| 650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
| 650 | 4 | $0MAPA20080564322$aTarificación | |
| 650 | 4 | $0MAPA20080603779$aSeguro de automóviles | |
| 700 | 1 | $0MAPA20260002989$aGawlowski, Karol | |
| 773 | 0 | $wMAP20200013259$g02/02/2026 Number 7 - February 2026 , p. 32 - 35$tThe Actuary : the magazine of the Institute & Faculty of Actuaries$dLondon : Redactive Publishing, 2019- |