博客
关于我
《机器学习与实践》读书笔记及代码(三)
阅读量:146 次
发布时间:2019-02-27

本文共 2260 字,大约阅读时间需要 7 分钟。

#波士顿地区,用线性回归,去预测房价from sklearn.datasets import load_bostonboston = load_boston()print boston.DESCRfrom sklearn.cross_validation import train_test_splitimport numpy as npX = boston.datay = boston.target#如果没有这里的话,下一步会报错# X.shapeX_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)# print X_train.shape# print y_train.shape#从上面当中,显然发现预测目标房价之间,差距很大,因此,应该先标准化处理from sklearn.preprocessing import StandardScalerss_X = StandardScaler()#分别对训练和测试数据的特征,以及目标值进行标准化处理X_train = ss_X.fit_transform(X_train)X_test = ss_X.transform(X_test)ss_y = StandardScaler()#这里一定要有reshape(-1,1)这样一个过程,否则会报错,y_train = ss_y.fit_transform(y_train.reshape(-1, 1))y_test = ss_y.transform(y_test.reshape(-1, 1))#此处使用十分简单的LinearRegression和SGDRegression分别对美国波士顿地区的房价进行预测from sklearn.linear_model import LinearRegressionlr = LinearRegression()lr.fit(X_train,y_train)lr_y_predict = lr.predict(X_test)from sklearn.linear_model import SGDRegressorsgdr = SGDRegressor()sgdr.fit(X_train,y_train)sgdr_y_predict = sgdr.predict(X_test)#使用LinearRegression模型自带的评估模块。并输出结果print 'The value of default measurement of LinearRegression is:',lr.score(X_test,y_test)from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_errorprint 'The value of R-squared of LinearRegression is:',r2_score(y_test,lr_y_predict)print 'The mean squared error of LinearRegression is:',mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(lr_y_predict))print 'The mean absolute error of LinearRegression is:',mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(lr_y_predict))#使用SGDRegression模型自带的评估模块。并输出结果print 'The value of default measurement of SGDRegressor is:',sgdr.score(X_test,y_test)print 'The value of R-squared of LinearRegression is:',r2_score(y_test,sgdr_y_predict)print 'The mean squared error of LinearRegression is:',mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(sgdr_y_predict))print 'The mean absolute error of LinearRegression is:',mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(sgdr_y_predict))

支持向量机(回归)

 

转载地址:http://ixjb.baihongyu.com/

你可能感兴趣的文章
MySQL Connector/Net 句柄泄露
查看>>
multiprocessor(中)
查看>>
mysql CPU使用率过高的一次处理经历
查看>>
Multisim中555定时器使用技巧
查看>>
MySQL CRUD 数据表基础操作实战
查看>>
multisim变压器反馈式_穿过隔离栅供电:认识隔离式直流/ 直流偏置电源
查看>>
mysql csv import meets charset
查看>>
multivariate_normal TypeError: ufunc ‘add‘ output (typecode ‘O‘) could not be coerced to provided……
查看>>
MySQL DBA 数据库优化策略
查看>>
multi_index_container
查看>>
MySQL DBA 进阶知识详解
查看>>
Mura CMS processAsyncObject SQL注入漏洞复现(CVE-2024-32640)
查看>>
Mysql DBA 高级运维学习之路-DQL语句之select知识讲解
查看>>
mysql deadlock found when trying to get lock暴力解决
查看>>
MuseTalk如何生成高质量视频(使用技巧)
查看>>
mutiplemap 总结
查看>>
MySQL DELETE 表别名问题
查看>>
MySQL Error Handling in Stored Procedures---转载
查看>>
MVC 区域功能
查看>>
MySQL FEDERATED 提示
查看>>