July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Content preview from Python机器学习手册：从数据预处理到深度学习
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252
｜
第
14
章
树和森林
max_leaf_nodes
最大叶子节点数。
min_impurity_split
执行分裂所需的最小不纯度减少量。
了解这些参数很有用，不过一般情况下，我们只会用到
max_depth
和
min_impurity_
split
这两个参数，因为较浅的树（有时称为
树桩
，
stump
）结构更简单，并且方差较小。
14.10
通过
boosting
提高性能
问题描述
需要一个比决策树或随机森林性能更好的模型。
解决方案
使用
AdaBoostClassier
或
AdaBoostRegressor
训练一个
boosting
模型 ：
#
加载库
from sklearn.ensemble import AdaBoostClassifier
from sklearn import datasets
#
加载数据
iris = datasets.load_iris()
features = iris.data
target = iris.target
#
创建
adaboost
树分类器对象
adaboost = AdaBoostClassifier(random_state=0)
#
训练模型
model = adaboost.fit(features, target)
讨论
在随机森林中，用一组随机决策树对目标向量进行预测。而一种替代的且更强大的学习
方法称为
boosting
。有一种形式的
boosting
算法叫作
AdaBoost
，它迭代地训练一系列弱
模型（通常是一个浅层决策树，有时称为树桩） ...
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