利用SVM回归模型预测练习，数据集是sklearn集成的美国波士顿地区房价。kernel=‘rbf’，指用径向基核函数配置的支持向量机进行回归训练。上码：

#-*- coding:utf-8 -*-

from sklearn.datasets import load_boston

boston=load_boston()

print(boston.DESCR)

from sklearn.cross_validation import train_test_split

import numpy as np

X=boston.data

y=boston.target

X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33,test_size=0.25)

print('The max target value is:',np.max(boston.target))

print('The min target value is:',np.min(boston.target))

print('The average target valueis:',np.mean(boston.target))

from sklearn.preprocessing import StandardScaler

ss_X=StandardScaler()

ss_y=StandardScaler()

X_train=ss_X.fit_transform(X_train)

X_test=ss_X.transform(X_test)

y_train=ss_y.fit_transform(y_train)

y_test=ss_y.transform(y_test)

from sklearn.svm import SVR

rbf_svr=SVR(kernel='rbf')

rbf_svr.fit(X_train,y_train)

rbf_svr_y_predict=rbf_svr.predict(X_test)

from sklearn.metrics importr2_score,mean_absolute_error,mean_squared_error

print('R-squared value of RBF SVRis:',r2_score(y_test,rbf_svr_y_predict))

print('The mean square error of RBF SVRis:',mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(rbf_svr_y_predict)))

print('The mean absolute error of RBF SVRis:',mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(rbf_svr_y_predict)))