from sklearn.ensemble import BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
for cls in [BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier]:
    est = cls(bootstrap=False, oob_score=True)
    try:
        est.fit(X, y)
    except ValueError as e:
        print(f"{cls.__name__}: {e}")

from sklearn.ensemble import BaggingRegressor, ExtraTreesRegressor, RandomForestRegressor
from sklearn.datasets import make_regression
X, y = make_regression(n_samples=200, n_features=4, n_informative=2)
for cls in [BaggingRegressor, ExtraTreesRegressor, RandomForestRegressor]:
    est = cls(bootstrap=False, oob_score=True)
    try:
        est.fit(X, y)
    except ValueError as e:
        print(f"{cls.__name__}: {e}")