机器学习之路：python 集成回归模型 随机森林回归RandomForestRegressor 极端随机森林回归ExtraTreesRegressor GradientBoostingRegressor回归 预测波士顿房价

python3 学习机器学习api

使用了三种集成回归模型

git: https://github.com/linyi0604/MachineLearning

代码：

 from sklearn.datasets import load_boston
 from sklearn.cross_validation import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressor
 from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
 import numpy as np

 '''
 随机森林回归
 极端随机森林回归
 梯度提升回归

 通常集成模型能够取得非常好的表现
 '''

 # 1 准备数据
 # 读取波士顿地区房价信息
 boston = load_boston()
 # 查看数据描述
 # print(boston.DESCR)   # 共506条波士顿地区房价信息，每条13项数值特征描述和目标房价
 # 查看数据的差异情况
 # print("最大房价：", np.max(boston.target))   # 50
 # print("最小房价：",np.min(boston.target))    # 5
 # print("平均房价：", np.mean(boston.target))   # 22.532806324110677

 x = boston.data
 y = boston.target

 # 2 分割训练数据和测试数据
 # 随机采样25%作为测试 75%作为训练
 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=33)

 # 3 训练数据和测试数据进行标准化处理
 ss_x = StandardScaler()
 x_train = ss_x.fit_transform(x_train)
 x_test = ss_x.transform(x_test)

 ss_y = StandardScaler()
 y_train = ss_y.fit_transform(y_train.reshape(-1, 1))
 y_test = ss_y.transform(y_test.reshape(-1, 1))

 # 4 三种集成回归模型进行训练和预测
 # 随机森林回归
 rfr = RandomForestRegressor()
 # 训练
 rfr.fit(x_train, y_train)
 # 预测 保存预测结果
 rfr_y_predict = rfr.predict(x_test)

 # 极端随机森林回归
 etr = ExtraTreesRegressor()
 # 训练
 etr.fit(x_train, y_train)
 # 预测 保存预测结果
 etr_y_predict = rfr.predict(x_test)

 # 梯度提升回归
 gbr = GradientBoostingRegressor()
 # 训练
 gbr.fit(x_train, y_train)
 # 预测 保存预测结果
 gbr_y_predict = rfr.predict(x_test)

 # 5 模型评估
 # 随机森林回归模型评估
 print("随机森林回归的默认评估值为：", rfr.score(x_test, y_test))
 print("随机森林回归的R_squared值为：", r2_score(y_test, rfr_y_predict))
 print("随机森林回归的均方误差为:", mean_squared_error(ss_y.inverse_transform(y_test),
                                           ss_y.inverse_transform(rfr_y_predict)))
 print("随机森林回归的平均绝对误差为:", mean_absolute_error(ss_y.inverse_transform(y_test),
                                              ss_y.inverse_transform(rfr_y_predict)))

 # 极端随机森林回归模型评估
 print("极端随机森林回归的默认评估值为：", etr.score(x_test, y_test))
 print("极端随机森林回归的R_squared值为：", r2_score(y_test, gbr_y_predict))
 print("极端随机森林回归的均方误差为:", mean_squared_error(ss_y.inverse_transform(y_test),
                                             ss_y.inverse_transform(gbr_y_predict)))
 print("极端随机森林回归的平均绝对误差为:", mean_absolute_error(ss_y.inverse_transform(y_test),
                                                ss_y.inverse_transform(gbr_y_predict)))

 # 梯度提升回归模型评估
 print("梯度提升回归回归的默认评估值为：", gbr.score(x_test, y_test))
 print("梯度提升回归回归的R_squared值为：", r2_score(y_test, etr_y_predict))
 print("梯度提升回归回归的均方误差为:", mean_squared_error(ss_y.inverse_transform(y_test),
                                             ss_y.inverse_transform(etr_y_predict)))
 print("梯度提升回归回归的平均绝对误差为:", mean_absolute_error(ss_y.inverse_transform(y_test),
                                                ss_y.inverse_transform(etr_y_predict)))

 '''
 随机森林回归的默认评估值为： 0.8391590262557747
 随机森林回归的R_squared值为： 0.8391590262557747
 随机森林回归的均方误差为: 12.471817322834646
 随机森林回归的平均绝对误差为: 2.4255118110236227

 极端随机森林回归的默认评估值为： 0.783339502805047
 极端随机森林回归的R_squared值为： 0.8391590262557747
 极端随机森林回归的均方误差为: 12.471817322834646
 极端随机森林回归的平均绝对误差为: 2.4255118110236227

 GradientBoostingRegressor回归的默认评估值为： 0.8431187344932869
 GradientBoostingRegressor回归的R_squared值为： 0.8391590262557747
 GradientBoostingRegressor回归的均方误差为: 12.471817322834646
 GradientBoostingRegressor回归的平均绝对误差为: 2.4255118110236227
 '''