November 2019
Intermediate to advanced
320 pages
13h 35m
French
Content preview from Machine Learning avec Scikit-Learn
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7.4 Forêts aléatoires
189
© Dunod – Toute reproduction non autorisée est un délit.
de régression. Le code qui suit entraîne un classicateur de forêt aléatoire avec 500 arbres
(chacun limité à 16 nœuds maximum), en utilisant tous les cœurs CPU disponibles:
from sklearn.ensemble import RandomForestClassifier
rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16,
n_jobs=-1)
rnd_clf.fit(X_train, y_train)
y_pred_rf = rnd_clf.predict(X_test)
Un RandomForestClassifier possède presque tous les hyperparamètres
d’un DecisionTreeClassifier (pour contrôler la création des arbres) et ceux
d’un BaggingClassifier (pour contrôler l’ensemble lui-même) ...
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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.