from sklearn.datasets import load_boston
from sklearn.svm import SVR
from sklearn.cross_validation import train_test_split
from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error
from sklearn.preprocessing import StandardScaler

导入相关数据集

boston = load_boston()

显示数据集的详细描述信息

print(boston.DESCR)
X = boston.data
y = boston.target

将数据划分为训练集和测试集

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

对训练集和测试集的特征变量与目标变量进行标准化处理，以提升模型性能和稳定性

X_train = ss_X.fit_transform(