Regression.py

#coding=utf-8
from sklearn.datasets import load_boston
from sklearn.cross_validation import train_test_split
import warnings
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import SGDRegressor
from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error
from sklearn.svm import SVR

#数据的标准化(normalization)是将数据按比例缩放，使之落入一个小的特定区间。 在某些比较和评价的指标处理中经常会用到，去除数据的单位限制，将其转化为无量纲的纯数值，便于不同单位或量级的指标能够进行比较和加权。
warnings.filterwarnings("ignore")
boston=load_boston()
print(boston.DESCR)
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 value is ',np.mean(boston.target))
ss_X=StandardScaler()
ss_y=StandardScaler()
X_train=ss_X.fit_transform(X_train)
X_test=ss_X.transform(X_test)
lr=LinearRegression()
lr.fit(X_train,y_train)
lr_y_predict=lr.predict(X_test)
sgdr=SGDRegressor()
sgdr.fit(X_train,y_train)
sgdr_y_predict=sgdr.predict(X_test)
print('The value of default measurement of LinearRegression is ',lr.score(X_test,y_test))
linear_svr = SVR(kernel='linear')
linear_svr.fit(X_train,y_train)
linear_svr_y_predict = linear_svr.predict(X_test)
poly