有趣的边缘行为。在这个例子中，KNN exists会被打印出来，但Random Forest exists却不会。
在检查模型是否存在时发现了这个问题，当模型是随机森林时，if model: ...不会被触发。
from sklearn.ensemble import RandomForestClassifierfrom sklearn.neighbors import KNeighborsClassifierif KNeighborsClassifier(4): print('KNN exists')if RandomForestClassifier(n_estimators=10, max_depth=4): print('Random Forest exists')为什么会这样呢？
回答：
啊哈！这是因为Random实现了__len__方法：
In [1]: from sklearn.ensemble import RandomForestClassifier ...: from sklearn.neighbors import KNeighborsClassifier ...:In [2]: knn = KNeighborsClassifier(4)In [3]: forest = RandomForestClassifier(n_estimators=10, max_depth=4)In [4]: knn.__bool__---------------------------------------------------------------------------AttributeError Traceback (most recent call last)<ipython-input-4-ef1cfe16be77> in <module>()----> 1 knn.__bool__AttributeError: 'KNeighborsClassifier' object has no attribute '__bool__'In [5]: knn.__len__---------------------------------------------------------------------------AttributeError Traceback (most recent call last)<ipython-input-5-dc98bf8c50e0> in <module>()----> 1 knn.__len__AttributeError: 'KNeighborsClassifier' object has no attribute '__len__'In [6]: forest.__bool__---------------------------------------------------------------------------AttributeError Traceback (most recent call last)<ipython-input-6-fbdd7f01e843> in <module>()----> 1 forest.__bool__AttributeError: 'RandomForestClassifier' object has no attribute '__bool__'In [7]: forest.__len__Out[7]:<bound method BaseEnsemble.__len__ of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=4, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1, oob_score=False, random_state=None, verbose=0, warm_start=False)>In [8]: len(forest)Out[8]: 0根据Python数据模型的描述：
object.__bool__(self)用于实现真值测试和内置操作
bool()；应该返回False或True。当此方法未定义时，如果定义了__len__()，则会调用它，如果其结果非零，则认为对象为真。如果一个类既没有定义__len__()也没有定义__bool__()，那么它的所有实例都被认为是真的。
正如预期的那样，RandomForestClassifier的len是估计器的数量，但只有在.fit之后才会这样：
In [9]: from sklearn.datasets import make_classification ...: X, y = make_classification(n_samples=1000, n_features=4, ...: n_informative=2, n_redundant=0, ...: random_state=0, shuffle=False) ...:In [10]: X.shapeOut[10]: (1000, 4)In [11]: y.shapeOut[11]: (1000,)In [12]: forest.fit(X,y)Out[12]:RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=4, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1, oob_score=False, random_state=None, verbose=0, warm_start=False)In [13]: len(forest)Out[13]: 10