极端随机树 - Extremely_Randomized_Trees

什么是极端随机树 (Extremely Randomized Trees)?

• 

• 一种集成学习方法，它通过构建多个决策树并结合它们的预测结果来做出最终决策

   >>> from sklearn.model_selection import cross_val_score
   >>> from sklearn.datasets import make_blobs
   >>> from sklearn.ensemble import RandomForestClassifier
   >>> from sklearn.ensemble import ExtraTreesClassifier
   >>> from sklearn.tree import DecisionTreeClassifier
  
   >>> X, y = make_blobs(n_samples=10000, n_features=10, centers=100,
  ...     random_state=0)
  
   >>> clf = DecisionTreeClassifier(max_depth=None, min_samples_split=2,
  ...     random_state=0)
   >>> scores = cross_val_score(clf, X, y, cv=5)
   >>> scores.mean()
  0.98...
  
   >>> clf = RandomForestClassifier(n_estimators=10, max_depth=None,
  ...     min_samples_split=2, random_state=0)
   >>> scores = cross_val_score(clf, X, y, cv=5)
   >>> scores.mean()
  0.999...
  
   >>> clf = ExtraTreesClassifier(n_estimators=10, max_depth=None,
  ...     min_samples_split=2, random_state=0)
   >>> scores = cross_val_score(clf, X, y, cv=5)
   >>> scores.mean() > 0.999
  True

• 

在 scikit-learn 中，随机极端树如何应用于分类任务？

• 该类实现了一个元估计器，它在数据集的各种子样本上拟合了一些随机决策树（又称树外树），并使用平均法来提高预测精度和控制过度拟合

   >>> from sklearn.ensemble import ExtraTreesClassifier
   >>> from sklearn.datasets import make_classification
   >>> X, y = make_classification(n_features=4, random_state=0)
   >>> clf = ExtraTreesClassifier(n_estimators=100, random_state=0)
   >>> clf.fit(X, y)
  ExtraTreesClassifier(random_state=0)
   >>> clf.predict([[0, 0, 0, 0]])
  array([1])

在 scikit-learn 中，随机极端树如何应用于回归任务？

• 该类实现了一个元估计器，它在数据集的各种子样本上拟合了一些随机决策树（又称树外树），并使用平均法来提高预测精度和控制过度拟合

   >>> from sklearn.datasets import load_diabetes
   >>> from sklearn.model_selection import train_test_split
   >>> from sklearn.ensemble import ExtraTreesRegressor
   >>> X, y = load_diabetes(return_X_y=True)
   >>> X_train, X_test, y_train, y_test = train_test_split(
  ...     X, y, random_state=0)
   >>> reg = ExtraTreesRegressor(n_estimators=100, random_state=0).fit(
  ...    X_train, y_train)
   >>> reg.score(X_test, y_test)
  0.2708...