python实现集成回归算法,包括随机森林,极端随机森林,梯度boosting算法
来源:互联网 发布:淘宝可以参加聚划算吗 编辑:程序博客网 时间:2024/06/10 01:33
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.ensemble import RandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressorrfr = RandomForestRegressor()rfr.fit(X_test, y_test)rfr_y_predict = rfr.predict(X_test)etr = ExtraTreesRegressor()etr.fit(X_train, y_train)etr_y_predict = etr.predict(X_test)gbr = GradientBoostingRegressor()gbr.fit(X_train, y_train)gbr_y_predict = gbr.predict(X_test)from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_errorprint 'R-squared value of RandomForestRegressor is: ', rfr.score(X_test, y_test)print 'The mean squared error of RandomForestRegressor is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(rfr_y_predict))print 'The mean absolute error of RandomForestRegressor is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(rfr_y_predict))print 'R-squared of ExtraTreesRegressor is: ', etr.score(X_test, y_test)print 'the value of mean squared error of ExtraTreesRegressor is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(etr_y_predict))print 'the value of mean ssbsolute error of ExtraTreesRegressor is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(etr_y_predict))print 'R-squared of GradientBoostingRegressor is: ', gbr.score(X_test, y_test)print 'the value of mean squared error of GradientBoostingRegressor is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(gbr_y_predict))print 'the value of mean ssbsolute error of GradientBoostingRegressor is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(gbr_y_predict))
阅读全文
0 0
- python实现集成回归算法,包括随机森林,极端随机森林,梯度boosting算法
- 随机森林算法实现
- 随机森林算法实现
- 分类&回归算法-随机森林
- 集成模型python实现,随机森林,梯度提升决策树
- 随机森林算法的python实现
- 随机森林算法入门(python)
- 随机森林算法入门(python)
- 机器学习算法---随机森林实现(包括回归和分类)
- 随机森林算法
- 随机森林算法
- 随机森林算法
- 随机森林算法
- 随机森林算法
- 随机森林算法
- 随机森林算法
- 随机森林算法
- 随机森林算法
- golang walk界面库 最小化事件
- python实现kmeans算法
- 前端之jquery动画应用
- 原文:一套HTML网站后台信息管理静态网页模版下载 源代码下载地址:http://www.zuidaima.com/share/1821271068036096.htm 体验地址:http://18
- HRBUSTOJ 1313 火影忍者之~静音
- python实现集成回归算法,包括随机森林,极端随机森林,梯度boosting算法
- python实现K近邻回归,采用等权重和不等权重
- 认识 memcached
- python实现支持向量回归,包括线性,多项式,径向基
- ZJCOJ qwb与神奇的序列 构造矩阵 or 递推
- HTML/CSS速写神器Emmet
- C++中的函数参数
- HashMap与TreeMap
- 闭包 匿名函数的调用 链式作用域 预解析机制