January 2018
Intermediate to advanced
301 pages
8h 54m
Chinese
Content preview from Python机器学习基础教程
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176
｜
第
4
章
图 4-6：对于 RBF 核的 SVM，使用不同
gamma
参数的对比
使用更加复杂的模型（即核
SVM
），我们能够学到一个与多项式回归的复杂度类似的预测
结果，且不需要进行显式的特征变换。
我们再次观察波士顿房价数据集，作为对交互特征和多项式特征更加实际的应用。我们在
第
2
章已经在这个数据集上使用过多项式特征了。现在来看一下这些特征的构造方式，以
及多项式特征的帮助有多大。首先加载数据，然后利用 MinMaxScaler 将其缩放到
0
和
1
之间：
In[27]:
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split\
(boston.data, boston.target, random_state=0)
# 缩放数据
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
下面我们提取多项式特征和交互特征，次数最高为
2
：
In[28]: ...
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