python实现房价预测,采用回归和随机梯度下降法
来源:互联网 发布:淘宝如何注册账号 编辑:程序博客网 时间:2024/05/23 23:00
from sklearn.datasets import load_bostonboston = load_boston()from sklearn.cross_validation import train_test_splitimport numpy as np;X = boston.datay = boston.targetX_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 terget value is: ', np.mean(boston.target)from sklearn.preprocessing import StandardScalerss_X = StandardScaler()ss_y = StandardScaler()X_train = ss_X.fit_transform(X_train)X_test = ss_X.transform(X_test)y_train = ss_y.fit_transform(y_train)y_test = ss_y.transform(y_test)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)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))print 'The value of default measurement of SGDRegression is: ', sgdr.score(X_test, y_test)print 'The value of R-squared of SGDRegression is: ', r2_score(y_test, sgdr_y_predict)print 'the value of mean squared error of SGDRgression is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_predict))print 'the value of mean ssbsolute error of SGDRgression is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_predict))
阅读全文
0 0
- python实现房价预测,采用回归和随机梯度下降法
- 逻辑回归-梯度下降法 python实现
- python实现随机梯度下降法
- 梯度下降法 线性回归 多项式回归 python实现
- 梯度下降法求解线性回归之python实现
- 线性回归参数估计--最小二乘法与梯度下降法Python实现
- Python实现 线性回归(梯度下降)
- 线性回归和批量梯度下降法python
- 批量梯度下降和随机梯度下降matlab 实现
- python实现随机梯度下降(SGD)
- 逻辑回归python实现(随机增量梯度下降,变步长)
- 随机梯度下降法,批量梯度下降法和小批量梯度下降法以及代码实现
- 回归和梯度下降
- 回归和梯度下降
- 随机梯度下降法和批量梯度下降法
- 梯度下降法和随机梯度下降法的理解
- 批梯度下降法和随机梯度下降法(SGD)
- 梯度下降法和随机梯度下降法的区别
- mysql必知必会学习笔记
- ORACLE数据库SQL语句的执行过程
- PAT--1032. Sharing
- JAVA设计模式(单例模式)
- ThreadLocal线程本地变量
- python实现房价预测,采用回归和随机梯度下降法
- socket之TCP多线程客户服务器编程
- Joseph
- org.springframework.beans.factory.BeanCreationException: Error creating bean with name
- 集成模型python实现,随机森林,梯度提升决策树
- hadoop配置及测试中错误归纳
- 浅谈设计模式--单例模式(Singleton Pattern)
- ZOJ--1101:Gamblers(二分查找)
- MPI实现psrs