April 2025
Intermediate to advanced
416 pages
8h 32m
Portuguese (Portugal, Brazil)
Content preview from Livro de receitas de aprendizagem automática com Python, 2ª edição
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Capítulo 10. Redução de dimensionalidade usando seleção de caraterísticas
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10.0 Introdução
No Capítulo 9, discutimos a forma de reduzir a dimensionalidade da nossa matriz de caraterísticas, criando novas caraterísticas com capacidades (idealmente) semelhantes para treinar modelos de qualidade, mas com dimensões significativamente menores. A isto chama-se extração de caraterísticas. Neste capítulo, abordaremos uma abordagem alternativa: selecionar caraterísticas informativas e de elevada qualidade e eliminar as caraterísticas menos úteis. Chama-se a isto seleção de caraterísticas.
Existem três tipos de métodos de seleção de caraterísticas: filtro, invólucro e incorporado. Os métodos de filtragem selecionam as melhores caraterísticas examinando as suas propriedades estatísticas. Os métodos em que definimos explicitamente um limiar para uma estatística ou seleccionamos manualmente o número de caraterísticas que queremos manter são exemplos de seleção de caraterísticas por filtragem. Os métodos de invólucro utilizam a tentativa e erro para encontrar o subconjunto de caraterísticas que produz modelos com as previsões de maior qualidade. Os métodos envolventes são frequentemente os mais eficazes, uma vez que encontram o melhor resultado através da experimentação real, por oposição a suposições ingénuas. Finalmente, os métodos incorporados selecionam o melhor subconjunto ...
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