本文整理汇总了Python中sklearn.ensemble.BaggingClassifier._make_estimator方法的典型用法代码示例。如果您正苦于以下问题：Python BaggingClassifier._make_estimator方法的具体用法？Python BaggingClassifier._make_estimator怎么用？Python BaggingClassifier._make_estimator使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.ensemble.BaggingClassifier的用法示例。
在下文中一共展示了BaggingClassifier._make_estimator方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_base
# 需要导入模块: from sklearn.ensemble import BaggingClassifier [as 别名]
# 或者: from sklearn.ensemble.BaggingClassifier import _make_estimator [as 别名]
def test_base():
# Check BaseEnsemble methods.
ensemble = BaggingClassifier(
base_estimator=Perceptron(tol=1e-3, random_state=None), n_estimators=3)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
random_state = np.random.RandomState(3)
ensemble._make_estimator(random_state=random_state)
ensemble._make_estimator(random_state=random_state)
ensemble._make_estimator(append=False)
assert_equal(3, len(ensemble))
assert_equal(3, len(ensemble.estimators_))
assert_true(isinstance(ensemble[0], Perceptron))
assert_equal(ensemble[0].random_state, None)
assert_true(isinstance(ensemble[1].random_state, int))
assert_true(isinstance(ensemble[2].random_state, int))
assert_not_equal(ensemble[1].random_state, ensemble[2].random_state)
np_int_ensemble = BaggingClassifier(base_estimator=Perceptron(tol=1e-3),
n_estimators=np.int32(3))
np_int_ensemble.fit(iris.data, iris.target)示例2: test_base
# 需要导入模块: from sklearn.ensemble import BaggingClassifier [as 别名]
# 或者: from sklearn.ensemble.BaggingClassifier import _make_estimator [as 别名]
def test_base():
"""Check BaseEnsemble methods."""
ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=3)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator(append=False)
assert_equal(3, len(ensemble))
assert_equal(3, len(ensemble.estimators_))
assert_true(isinstance(ensemble[0], Perceptron))