random forest regressor python
from sklearn.ensemble import RandomForestRegressor
clf = RandomForestRegressor(max_depth=2, random_state=0)
clf.fit(X, y)
print(clf.predict([[0, 0, 0, 0]]))random forest regressor python
from sklearn.ensemble import RandomForestRegressor
clf = RandomForestRegressor(max_depth=2, random_state=0)
clf.fit(X, y)
print(clf.predict([[0, 0, 0, 0]]))random forest python
from sklearn.ensemble import RandomForestClassifier
y = train_data["Survived"]
features = ["Pclass", "Sex", "SibSp", "Parch","Fare","Age"]
X = pd.get_dummies(train_data[features])
X_test = pd.get_dummies(test_data[features])
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
model.fit(X, y)
predictions = model.predict(X_test)Copyright © 2021 Codeinu
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