random.py

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor

boston = load_boston()
X, y = boston.data, boston.target
X_train, X_test, y_train, y_test = train_test_split(
    X, y, train_size=0.6, test_size=0.4, random_state=0)
X_train = np.array(X_train, dtype=np.float32)
X_test = np.array(X_test, dtype=np.float32)

dt = DecisionTreeRegressor(random_state=0)
rf = RandomForestRegressor(
    random_state=0, bootstrap=False, max_depth=None)
rf.fit(X_train, y_train)
for est in rf.estimators_:
    print(est.predict(X_test[:3]))
    print("From tree")