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 17. Máquinas de vetor de suporte
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17.0 Introdução
Para entender as máquinas de vetor de suporte , precisamos entender os hiperplanos. Formalmente, um hiperplano é um subespaço n - 1 em um espaçon-dimensional. Embora pareça complexo, na realidade é bastante simples. Por exemplo, se quiséssemos dividir um espaço bidimensional, usaríamos um hiperplano unidimensional (ou seja, uma linha). Se quiséssemos dividir um espaço tridimensional, utilizaríamos um hiperplano bidimensional (ou seja, uma folha de papel plana ou um lençol). Um hiperplano é simplesmente uma generalização desse conceito para n dimensões.
As máquinas de vetor de suporte classificam os dados encontrando o hiperplano que maximiza a margem entre as classes nos dados de treinamento. Num exemplo bidimensional com duas classes, podemos pensar num hiperplano como a "banda" reta mais larga (ou seja, linha com margens) que separa as duas classes.
Neste capítulo, abordamos o treino de máquinas de vectores de suporte numa variedade de situações e analisamos como podemos alargar a abordagem para resolver problemas comuns.
17.1 Treinar um classificador linear
Problema
Tens de treinar um modelo para classificar as observações.
Solução
Utiliza um classificador de vetor de suporte (SVC) para encontrar o hiperplano que maximiza as margens entre as classes:
# Load librariesfromsklearn.svmimport
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