Machine learning with High-Cardinality categorical features in actuarial applications

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<dc:creator>Avanzi, Benjamin</dc:creator>
<dc:date>2024-05-15</dc:date>
<dc:description xml:lang="es">Sumario: High-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</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/186140.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Ciencias Actuariales y Financieras</dc:subject>
<dc:subject xml:lang="es">Machine learning</dc:subject>
<dc:subject xml:lang="es">Actuarios</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Machine learning with High-Cardinality categorical features in actuarial applications</dc:title>
<dc:relation xml:lang="es">En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 15/05/2024 Volumen 54 Número 2 - mayo 2024 , p.213-238</dc:relation>
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