March 2019
Intermediate to advanced
256 pages
4h 57m
French
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CHAPITRE 17 Réduction de la dimensionnalité
Plusieurs techniques sont disponibles pour décomposer des caractéristiques afin d’obtenir un sous-ensemble moins grand. Ce genre de traitement est utile dans les analyses exploratoires de données, dans la visualisation, dans la création de modèles prédictifs et dans le partitionnement ou regroupement (clustering).
Nous allons dans ce chapitre travailler sur le jeu de données Titanic pour découvrir plusieurs techniques : PCA, UMAP, t-SNE et PHATE.
Voici d’abord les données de travail :
>>> ti_df = tweak_titanic(orig_df) >>> std_cols = "pclass,age,sibsp,fare".split(",") >>> X_train, X_test, y_train, y_test = get_train_test_X_y( ... ti_df, "survived", std_cols=std_cols ... ) >>> X = pd.concat([X_train, X_test]) ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.