November 2019
Intermediate to advanced
320 pages
13h 35m
French
Content preview from Machine Learning avec Scikit-Learn
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8.5 LLE
219
© Dunod – Toute reproduction non autorisée est un délit.
Vous vous demandez peut-être comment effectuer cette reconstruction. Une
solution consiste à entraîner un modèle de régression supervisé, avec pour jeu d’en-
traînement les observations projetées et pour valeurs cibles (ou étiquettes) les obser-
vations d’origine. Scikit-Learn effectuera ceci automatiquement si vous dénissez
fit_inverse_transform=True, comme dans le code suivant
118
.
rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.0433,
fit_inverse_transform=True)
X_reduced = rbf_pca.fit_transform(X)
X_preimage = rbf_pca.inverse_transform(X_reduced)
Par défaut, fit_inverse_transform=False ...
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