机器学习（三）使用sklearn库的datasets练习LinearRegression

# -*- coding: utf-8 -*-
"""
Created on Tue Oct 25 20:42:24 2016

@author:Sirius
LinearRegression
"""

from sklearn import datasets
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import train_test_split
load_data=datasets.load_boston()
data_x=load_data.data
data_y=load_data.target

x_train,x_test,y_train,y_test=train_test_split(
                              data_x,data_y,test_size=0.1)#test_size是test数据集占的百分比

model=LinearRegression()
model.fit(x_train,y_train)

print model.predict(x_test)
print y_test

结果如下：

datasets如下：

也可以自己生成数据来练习：

# -*- coding: utf-8 -*-
"""
Created on Tue Oct 25 20:42:24 2016

@author:Sirius
LinearRegression
"""

from sklearn import datasets
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import train_test_split
import matplotlib.pyplot as plt


x,y=datasets.make_regression(n_samples=100,n_features=1,n_targets=1,noise=10)
plt.scatter(x,y)
plt.show()

model.fit(x[:80],y[:80])
yy=model.predict(x[80:-1])
plt.plot(yy,'blue',label='y_predict')
plt.plot(y[80:-1],'red',label='Y_test')
plt.legend()

noise是噪声强度

再看看预测结果

最后

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