November 2019
Intermediate to advanced
320 pages
13h 35m
French
Content preview from Machine Learning avec Scikit-Learn
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8.3 PCA
213
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8.3.6 Choisir le bon nombre de dimensions
Au lieu de choisir arbitrairement le nombre de dimensions auxquelles on va réduire,
il est plus simple de choisir un nombre de dimensions représentant une contribution
sufsamment importante à la variance (p. ex. 95%). À moins, bien sûr, que vous ne
souhaitiez réduire la dimension en vue d’une visualisation des données. Dans ce cas,
vous souhaiterez réduire à 2 ou 3 dimensions.
Le code suivant effectue une PCA sans réduction de dimension, puis calcule le nombre
de composantes requises pour préserver 95% de la variance du jeu d’entraînement: ...
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