Modelización de tasas de caídas utilizando machine learning: comparaci´on entre las técnicas de survival forest y cox propotional hazards
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https://doi.org/10.26360/2021_7Palabras clave:
análisis de supervivencia, machine learning, tasas de caídas, random survival forestResumen
Este estudio realiza un análisis comparativo del rendimiento de las técnicas de machine learning y tradicionales de análisis de supervivencia. Las técnicas comparadas son el tradicional modelo de Cox Proportional Hazards (CPH) y las técnicas de machine learning Random Survival Forest (RSF) y Conditional Inference Forest (CIF). Estas técnicas se aplican para el estudio de una cartera de seguros de una de las Compañías más grandes de Seguros de Ecuador. Este estudio demuestra un mejor rendimiento de las técnicas de machine learning en la predicción de la función de supervivencia medidos a través del C-index y el Brier Score. También se demuestra que la aportación predictiva de las covariables en el modelo RSF es consistente con el modelo tradicional CPH.
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Derechos de autor 2022 Jorge Luis Andrade, José Luis Valencia
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
