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
209
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Figure 8.6 – La frontière de décision n’est pas toujours plus simple en dimension plus réduite
8.3 PCA
L’analyse en composantes principales (en anglais, principal component analysis, ou
PCA
114
) est de loin l’algorithme de réduction de dimension le plus connu. Il identie
tout d’abord l’hyperplan le plus proche des données, puis projette les données sur
celui-ci, tout comme sur la gure8.2.
8.3.1 Préservation de la variance
Pour pouvoir projeter le jeu d’entraînement sur un hyperplan de moindre dimension,
vous devez d’abord choisir le bon hyperplan. À titre d’exemple, un jeu de ...
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