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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14.8
处理不均衡的分类
｜
249
SelectFromModel
方法来创建特征矩阵，其中仅包含重要性大于或等于某阈值的特征。
最后，使用这些特征创建一个新模型。
这种方法中有两点需要注意 ：第一，经过
one-hot
编码的
nominal
型分类特征的重要性
被稀释到二元特征中 ；第二，一对高度相关的特征，其重要性被集中在其中一个特征上，
而不是均匀分布在这两个特征上。
延伸阅读
y
《使用随机森林进行特征选择》（
Robin Genuer
、
Jean-Michel Poggi
、
Christine
Tuleau-Malot
，
http://bit.ly/2FvG70D
）
14.8
处理不均衡的分类
问题描述
在高度不均衡的数据上训练随机森林模型。
解决方案
用参数
class_weight="balanced"
训练决策树或随机森林模型 ：
#
加载库
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets
#
加载数据
iris = datasets.load_iris()
features = iris.data
target = iris.target
#
删除前
40
个样本以获得高度不均衡的数据
features = features[40:,:]
target = target[40:]
#
创建目标向量表明分类为
0
还是
1
target = np.where((target ...
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