支持向量机SVM：SVR

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
boston=load_boston()
X,y=boston.data,boston.target
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=8)

from sklearn.svm import SVR
from sklearn.metrics import r2_score
for kernel in ['linear','rbf']:
    svr=SVR(kernel=kernel)
    svr.fit(X_train,y_train)
    print(kernel,'kernel,the train score is:{}'.format(svr.score(X_train,y_train)))
    print(kernel, 'kernel,the test score is:{}'.format(svr.score(X_test, y_test)))

import matplotlib.pyplot as plt
plt.plot(X.min(axis=0),'v',label='min')
plt.plot(X.max(axis=0),'^',label='max')
plt.yscale('log')
plt.legend(loc='best')
plt.xlabel('features')
plt.ylabel('feature magnitude')
plt.show()

from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
scaler.fit(X_train)
X_train_scaled=scaler.transform(X_train)
X_test_scaled=scaler.transform(X_test)

plt.plot(X_train_scaled.min(axis=0),'v',label='train set min')
plt.plot(X_train_scaled.max(axis=0),'^',label='train set max')
plt.plot(X_test_scaled.min(axis=0),'v',label='test set min')
plt.plot(X_test_scaled.max(axis=0),'^',label='test set max')
#plt.yscale('log')
plt.legend(loc='best')
plt.xlabel('features')
plt.ylabel('feature magnitude')
plt.show()

for kernel in ['linear','rbf']:
    svr=SVR(kernel=kernel)
    svr.fit(X_train_scaled,y_train)
    print(kernel,'kernel,the train score is:{}'.format(svr.score(X_train_scaled,y_train)))
    print(kernel, 'kernel,the test score is:{}'.format(svr.score(X_test_scaled, y_test)))

for kernel in ['linear','rbf']:
    svr=SVR(kernel=kernel,gamma=0.1,C=100)
    svr.fit(X_train_scaled,y_train)
    print(kernel,'kernel,the train score is:{}'.format(svr.score(X_train_scaled,y_train)))
    print(kernel, 'kernel,the test score is:{}'.format(svr.score(X_test_scaled, y_test)))