October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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第 6 章
决策树
与 SVM 一样，决策树是通用的机器学习算法，可以执行分类和回归任务，甚至多输出
任务。它们是功能强大的算法，能够拟合复杂的数据集。例如，在第 2 章中，你在加州
房屋数据集中训练了 DecisionTreeRegressor 模型，使其完全拟合（实际上是过
拟 合 ）。
决策树也是随机森林的基本组成部分（见第 7 章），它们是当今最强大的机器学习算法
之一。
在本章中，我们将从讨论如何使用决策树进行训练、可视化和做出预测开始。然后，我
们将了解 Scikit-Learn 使用的 CART 训练算法，并将讨论如何对树进行正则化并将其用
于回归任务。最后，我们将讨论决策树的一些局限性。
6.1 训练和可视化决策树
为了理解决策树，让我们建立一个决策树，然后看看它是如何做出预测的。以下代码在
鸢尾花数据集上训练了一个 DecisionTreeClassifier（见第 4 章 ）：
from sklearn.datasets import
load_iris
from sklearn.tree import
DecisionTreeClassifier
iris = load_iris()
X = iris.data[:, 2:]
# petal length and width
y = iris.target
tree_clf = DecisionTreeClassifier(max_depth=2)
tree_clf.fit(X, y)
要将决策树可视化，首先，使用 export_graphviz() ...
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