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from sklearn.ensemble import RandomForestClassifier # for classification
# or
from sklearn.ensemble import RandomForestRegressor # for regression
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from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(max_depth=2, random_state=0)
clf.fit(X, y)
print(clf.predict([[0, 0, 0, 0]]))
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from sklearn.ensemble import RandomForestClassifier
# Create a random forest classifier with 100 trees
rf_classifier = RandomForestClassifier(n_estimators=100)
# X is the feature matrix and y is the target variable
X = [[0, 0], [1, 1]]
y = [0, 1]
# Train the random forest classifier
rf_classifier.fit(X, y)
# Predict using the trained classifier
predictions = rf_classifier.predict([[2., 2.]])
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from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=1000, n_features=4,
n_informative=2, n_redundant=0,
random_state=0, shuffle=False)
clf = RandomForestClassifier(max_depth=2, random_state=0)
clf.fit(X, y)
print(clf.predict([[0, 0, 0, 0]]))
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from sklearn.ensemble import BaggingClassifier
from sklearn.neighbors import KNeighborsClassifier
bagging = BaggingClassifier(KNeighborsClassifier(),
max_samples=0.5, max_features=0.5)