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Machine learning with High-Cardinality categorical features in actuarial applications

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24510‎$a‎Machine learning with High-Cardinality categorical features in actuarial applications‎$c‎Benjamin Avanzi [et al.]
520  ‎$a‎High-cardinality categorical features are pervasive in actuarial data (e.g., occupation in commercial property insurance). Standard categorical encoding methods like one-hot encoding are inadequate in these settings
650 4‎$0‎MAPA20080627904‎$a‎Ciencias Actuariales y Financieras
650 4‎$0‎MAPA20170005476‎$a‎Machine learning
650 4‎$0‎MAPA20080549497‎$a‎Actuarios
700  ‎$0‎MAPA20090029927‎$a‎Avanzi, Benjamin
7730 ‎$w‎MAP20077000420‎$g‎15/05/2024 Volumen 54 Número 2 - mayo 2024 , p.213-238‎$x‎0515-0361‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association
856  ‎$u‎https://www.cambridge.org/core/journals/astin-bulletin-journal-of-the-iaa/article/machine-learning-with-highcardinality-categorical-features-in-actuarial-applications/C910580D669A903DA6CC00AE6CDCB4DE