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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13.2
处理特征之间的影响
｜
229
24560.23872370844
不错，预测值和真实值只相差
560.24
美元！
线性回归的主要优点是可解释性，因为模型的系数代表特征每单位的变化对目标向量的
影响。例如，本解决方案的第一个特征是每个居民的犯罪次数。在我们的模型中，这个
特征的系数约为
-
0.35
，如果将这个系数乘以
1000
（因为目标向量是以千美元计数的房
价），就可以计算人均犯罪每增加一次以后房价的变化 ：
#
第一个系数乘以
1000
model.coef_[0]*1000
-349.77588707748947
这表示人均犯罪量每增加
1
次会使房屋价格降低约
350
美元！
13.2
处理特征之间的影响
问题描述
一个特征对目标变量的影响取决于另一个特征，现在要处理两个特征之间的影响。
解决方案
使用
scikit-learn
的
PolynomialFeatures
创建多项式特征，对这种依赖关系建模 ：
#
加载库
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
from sklearn.preprocessing import PolynomialFeatures
#
加载只有两个特征的数据集
boston = load_boston()
features = boston.data[:,0:2]
target = boston.target ...
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