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Machine learning with High-Cardinality categorical features in actuarial applications

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      <subfield code="a">Machine learning with High-Cardinality categorical features in actuarial applications</subfield>
      <subfield code="c">Benjamin Avanzi [et al.]</subfield>
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      <subfield code="a">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</subfield>
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      <subfield code="g">15/05/2024 Volumen 54 Número 2 - mayo 2024 , p.213-238</subfield>
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      <subfield code="t">Astin bulletin</subfield>
      <subfield code="d">Belgium : ASTIN and AFIR Sections of the International Actuarial Association</subfield>
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      <subfield code="u">https://www.cambridge.org/core/journals/astin-bulletin-journal-of-the-iaa/article/machine-learning-with-highcardinality-categorical-features-in-actuarial-applications/C910580D669A903DA6CC00AE6CDCB4DE</subfield>
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