objectif

Forêt aléatoire pour classification ou régression.

code minimal

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
rf = RandomForestClassifier(n_estimators=50, random_state=0).fit(X, y)
print(len(rf.feature_importances_) == X.shape[1])

utilisation

from sklearn.ensemble import RandomForestRegressor
print(hasattr(RandomForestRegressor(), "fit"))

variante(s) utile(s)

from sklearn.metrics import accuracy_score
print(accuracy_score(y, rf.predict(X)) <= 1.0)

notes

• Augmenter n_estimators pour stabilité; fixer random_state.