ML之回归预测：利用13种机器学习算法对Boston(波士顿房价)数据集【13+1,506】进行回归预测(房价预测)来比较各模型性能
导读
通过利用13种机器学习算法，分别是LiR、kNN、SVR、DTR、RFR、SGDR、GBR、LGBR、XGBR算法，然后对Boston(波士顿房价)数据集，形状是【13+1,506】，进行回归预测(房价预测)来比较各模型性能，发现LGBR模型的性能最好。
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ML之回归预测：利用13种机器学习算法对Boston(波士顿房价)数据集【13+1,506】进行回归预测(房价预测)来比较各模型性能
目录
输出结果
设计思路
输出结果
新增第13种ML算法
数据的初步查验：输出回归目标值的差异The max target value is 50.0The min target value is 5.0The average target value is 22.532806324110677LiR：The value of default measurement of LiR is 0.6763403830998702LiR：R-squared value of DecisionTreeRegressor: 0.6763403830998702LiR：The mean squared error of DecisionTreeRegressor: 25.096985692067726LiR：The mean absoluate error of DecisionTreeRegressor: 3.5261239963985433kNNR_uni：The value of default measurement of kNNR_uni is 0.6903454564606561kNNR_uni：R-squared value of DecisionTreeRegressor: 0.6903454564606561kNNR_uni：The mean squared error of DecisionTreeRegressor: 24.01101417322835kNNR_uni：The mean absoluate error of DecisionTreeRegressor: 2.9680314960629928kNNR_dis：The value of default measurement of kNNR_dis is 0.7197589970156353kNNR_dis：R-squared value of DecisionTreeRegressor: 0.7197589970156353kNNR_dis：The mean squared error of DecisionTreeRegressor: 21.730250160926044kNNR_dis：The mean absoluate error of DecisionTreeRegressor: 2.8050568785108005linear_SVR：The value of default measurement of linear_SVR is 0.651717097429608linear_SVR：R-squared value of DecisionTreeRegressor: 0.651717097429608linear_SVR：The mean squared error of DecisionTreeRegressor: 27.0063071393243linear_SVR：The mean absoluate error of DecisionTreeRegressor: 3.426672916872753poly_SVR：The value of default measurement of poly_SVR is 0.40445405800289286poly_SVR：R-squared value of DecisionTreeRegressor: 0.4044540580028929poly_SVR：The mean squared error of DecisionTreeRegressor: 46.1794033139523poly_SVR：The mean absoluate error of DecisionTreeRegressor: 3.75205926674149rbf_SVR：The value of default measurement of rbf_SVR is 0.7564068912273935rbf_SVR：R-squared value of DecisionTreeRegressor: 0.7564068912273935rbf_SVR：The mean squared error of DecisionTreeRegressor: 18.888525000753493rbf_SVR：The mean absoluate error of DecisionTreeRegressor: 2.6075632979823276DTR：The value of default measurement of DTR is 0.699313885811367DTR：R-squared value of DecisionTreeRegressor: 0.699313885811367DTR：The mean squared error of DecisionTreeRegressor: 23.31559055118111DTR：The mean absoluate error of DecisionTreeRegressor: 3.1716535433070865RFR：The value of default measurement of RFR is 0.8320900865862684RFR：R-squared value of DecisionTreeRegressor: 0.8320900865862684RFR：The mean squared error of DecisionTreeRegressor: 13.019952055992995RFR：The mean absoluate error of DecisionTreeRegressor: 2.3392650918635174ETR：The value of default measurement of ETR is 0.7595247600325825ETR：R-squared value of DecisionTreeRegressor: 0.7595247600325824ETR：The mean squared error of DecisionTreeRegressor: 18.646761417322832ETR：The mean absoluate error of DecisionTreeRegressor: 2.5487401574803146SGDR：The value of default measurement of SGDR is 0.6525677025033261SGDR：R-squared value of DecisionTreeRegressor: 0.6525677025033261SGDR：The mean squared error of DecisionTreeRegressor: 26.940350120746693SGDR：The mean absoluate error of DecisionTreeRegressor: 3.524049659554681GBR：The value of default measurement of GBR is 0.8442966156976921GBR：R-squared value of DecisionTreeRegressor: 0.8442966156976921GBR：The mean squared error of DecisionTreeRegressor: 12.07344198657727GBR：The mean absoluate error of DecisionTreeRegressor: 2.2692783233003326[LightGBM] [Warning] feature_fraction is set=0.6, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6[LightGBM] [Warning] min_data_in_leaf is set=18, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=18[LightGBM] [Warning] min_sum_hessian_in_leaf is set=0.001, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=0.001[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7LGBR：The value of default measurement of LGBR is 0.824979251097139LGBR：R-squared value of DecisionTreeRegressor: 0.824979251097139LGBR：The mean squared error of DecisionTreeRegressor: 13.5713354452417LGBR：The mean absoluate error of DecisionTreeRegressor: 2.3653297699911455[0.6763403830998702, 0.6903454564606561, 0.7197589970156353, 0.651717097429608, 0.40445405800289286, 0.7564068912273935, 0.699313885811367, 0.8320900865862684, 0.7595247600325825, 0.6525677025033261, 0.8442966156976921, 0.824979251097139]{'learning_rate': 0.09, 'max_depth': 4, 'n_estimators': 200}rmse: 0.37116076328428194XGBR_grid：The value of default measurement of XGBR_grid is -0.1355992935386311XGBR_grid：R-squared value of DecisionTreeRegressor: 0.849406718248XGBR_grid：The mean squared error of DecisionTreeRegressor: 11.67719810423491XGBR_grid：The mean absoluate error of DecisionTreeRegressor: 2.156086404304805
设计思路
ML之回归预测：利用13种机器学习算法对Boston(波士顿房价)数据集【13+1 506】进行回归预测(房价预测)来比较各模型性能
