Machine learning with High-Cardinality categorical features in actuarial applications

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<title>Machine learning with High-Cardinality categorical features in actuarial applications</title>
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<abstract displayLabel="Summary">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</abstract>
<note type="statement of responsibility">Benjamin Avanzi [et al.]</note>
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<topic>Ciencias Actuariales y Financieras</topic>
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<url displayLabel="electronic resource" usage="primary display">https://www.cambridge.org/core/journals/astin-bulletin-journal-of-the-iaa/article/machine-learning-with-highcardinality-categorical-features-in-actuarial-applications/C910580D669A903DA6CC00AE6CDCB4DE</url>
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<publisher>Belgium : ASTIN and AFIR Sections of the International Actuarial Association</publisher>
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<identifier type="issn">0515-0361</identifier>
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<text>15/05/2024 Volumen 54 Número 2 - mayo 2024 , p.213-238</text>
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