Bagging

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import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import BaggingRegressor

X, y = fetch_california_housing(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=0
)

base = DecisionTreeRegressor(max_depth=None, random_state=0)
bagging = BaggingRegressor(
    estimator=base,
    n_estimators=100,
    max_samples=0.8,
    max_features=0.8,
    bootstrap=True,
    oob_score=True,
    random_state=0,
)
bagging.fit(X_train, y_train)

pred = bagging.predict(X_test)
print("RMSE:", mean_squared_error(y_test, pred, squared=False))
print("OOB score:", bagging.oob_score_)