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

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 50.0
The min target value is 5.0
The average target value is 22.532806324110677
 
 
LiR：The value of default measurement of LiR is 0.6763403830998702
LiR：R-squared value of DecisionTreeRegressor: 0.6763403830998702
LiR：The mean squared error of DecisionTreeRegressor: 25.096985692067726
LiR：The mean absoluate error of DecisionTreeRegressor: 3.5261239963985433
 
 
kNNR_uni：The value of default measurement of kNNR_uni is 0.6903454564606561
kNNR_uni：R-squared value of DecisionTreeRegressor: 0.6903454564606561
kNNR_uni：The mean squared error of DecisionTreeRegressor: 24.01101417322835
kNNR_uni：The mean absoluate error of DecisionTreeRegressor: 2.9680314960629928
kNNR_dis：The value of default measurement of kNNR_dis is 0.7197589970156353
kNNR_dis：R-squared value of DecisionTreeRegressor: 0.7197589970156353
kNNR_dis：The mean squared error of DecisionTreeRegressor: 21.730250160926044
kNNR_dis：The mean absoluate error of DecisionTreeRegressor: 2.8050568785108005
 
 
linear_SVR：The value of default measurement of linear_SVR is 0.651717097429608
linear_SVR：R-squared value of DecisionTreeRegressor: 0.651717097429608
linear_SVR：The mean squared error of DecisionTreeRegressor: 27.0063071393243
linear_SVR：The mean absoluate error of DecisionTreeRegressor: 3.426672916872753
poly_SVR：The value of default measurement of poly_SVR is 0.40445405800289286
poly_SVR：R-squared value of DecisionTreeRegressor: 0.4044540580028929
poly_SVR：The mean squared error of DecisionTreeRegressor: 46.1794033139523
poly_SVR：The mean absoluate error of DecisionTreeRegressor: 3.75205926674149
rbf_SVR：The value of default measurement of rbf_SVR is 0.7564068912273935
rbf_SVR：R-squared value of DecisionTreeRegressor: 0.7564068912273935
rbf_SVR：The mean squared error of DecisionTreeRegressor: 18.888525000753493
rbf_SVR：The mean absoluate error of DecisionTreeRegressor: 2.6075632979823276
 
 
DTR：The value of default measurement of DTR is 0.699313885811367
DTR：R-squared value of DecisionTreeRegressor: 0.699313885811367
DTR：The mean squared error of DecisionTreeRegressor: 23.31559055118111
DTR：The mean absoluate error of DecisionTreeRegressor: 3.1716535433070865
 
 
RFR：The value of default measurement of RFR is 0.8320900865862684
RFR：R-squared value of DecisionTreeRegressor: 0.8320900865862684
RFR：The mean squared error of DecisionTreeRegressor: 13.019952055992995
RFR：The mean absoluate error of DecisionTreeRegressor: 2.3392650918635174
 
 
ETR：The value of default measurement of ETR is 0.7595247600325825
ETR：R-squared value of DecisionTreeRegressor: 0.7595247600325824
ETR：The mean squared error of DecisionTreeRegressor: 18.646761417322832
ETR：The mean absoluate error of DecisionTreeRegressor: 2.5487401574803146
 
 
SGDR：The value of default measurement of SGDR is 0.6525677025033261
SGDR：R-squared value of DecisionTreeRegressor: 0.6525677025033261
SGDR：The mean squared error of DecisionTreeRegressor: 26.940350120746693
SGDR：The mean absoluate error of DecisionTreeRegressor: 3.524049659554681
GBR：The value of default measurement of GBR is 0.8442966156976921
GBR：R-squared value of DecisionTreeRegressor: 0.8442966156976921
GBR：The mean squared error of DecisionTreeRegressor: 12.07344198657727
GBR：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=0.7
LGBR：The value of default measurement of LGBR is 0.824979251097139
LGBR：R-squared value of DecisionTreeRegressor: 0.824979251097139
LGBR：The mean squared error of DecisionTreeRegressor: 13.5713354452417
LGBR：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: 0.37116076328428194
XGBR_grid：The value of default measurement of XGBR_grid is -0.1355992935386311
XGBR_grid：R-squared value of DecisionTreeRegressor: 0.8494067182200448
XGBR_grid：The mean squared error of DecisionTreeRegressor: 11.67719810423491
XGBR_grid：The mean absoluate error of DecisionTreeRegressor: 2.156086404304805

设计思路