ML之回归预测：利用九大类机器学习算法对无人驾驶系统参数(2018年的data,18+2)进行回归预测+评估九种模型性能

ML之回归预测：利用九大类机器学习算法对无人驾驶系统参数(2018年的data,18+2)进行回归预测+评估九种模型性能

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ML之回归预测：利用九大类机器学习算法对自动驾驶系统参数(2018年的data,18+2)进行回归预测+评估九种模型性能

输出记录

1、第一次输出错误记录

数据的初步查验：输出回归目标值的差异
The max target value is PeakNonedb    89
dtype: int64
The min target value is PeakNonedb    56
dtype: int64
The average target value is PeakNonedb    63.392157
dtype: float64

LiR LiR：The value of default measurement of LiR is 0.5231458055883893
LiR：R-squared value of DecisionTreeRegressor: 0.5231458055883893
LiR：The mean squared error of DecisionTreeRegressor: 25.294980943838745
LiR：The mean absoluate error of DecisionTreeRegressor: 3.608855227697136
LiR：测试141~153行数据， 
 [[ 747.01164105]
 [1534.72506527]
 [2569.73860794]
 [3646.40436281]
 [1579.9293663 ]
 [2860.34593738]
 [3736.26316737]
 [3506.55843101]
 [3519.97015753]
 [3565.68403454]
 [3666.57047459]
 [3700.74687407]]
kNN

kNNR_uni：The value of default measurement of kNNR_uni is 0.5866024699259604
kNNR_uni：R-squared value of DecisionTreeRegressor: 0.5866024699259604
kNNR_uni：The mean squared error of DecisionTreeRegressor: 21.928888888888892
kNNR_uni：The mean absoluate error of DecisionTreeRegressor: 3.2111111111111117
kNNR_uni：测试141~153行数据， 
 [[0.64097829]
 [0.64097829]
 [0.64097829]
 [0.64097829]
 [0.64097829]
 [0.64097829]
 [0.64097829]
 [0.64097829]
 [0.64097829]
 [0.64097829]
 [0.64097829]
 [0.64097829]]

kNNR_dis：The value of default measurement of kNNR_dis is 0.6601811947182363
kNNR_dis：R-squared value of DecisionTreeRegressor: 0.6601811947182363
kNNR_dis：The mean squared error of DecisionTreeRegressor: 18.02586682616158
kNNR_dis：The mean absoluate error of DecisionTreeRegressor: 2.847311210973169
kNNR_dis：测试141~153行数据， 
 [[0.64115297]
 [0.64098617]
 [0.64095601]
 [0.64095674]
 [0.64098432]
 [0.64095655]
 [0.64095685]
 [0.64095705]
 [0.64095705]
 [0.64095715]
 [0.6409569 ]
 [0.64095699]]
SVM

linear_SVR：The value of default measurement of linear_SVR is 0.22831988148305604
linear_SVR：R-squared value of DecisionTreeRegressor: 0.22831988148305604
linear_SVR：The mean squared error of DecisionTreeRegressor: 40.93417678062064
linear_SVR：The mean absoluate error of DecisionTreeRegressor: 4.02480324908603
linear_SVR：测试141~153行数据， 
 [177.03008956 325.6232456  539.28353027 735.51485927 323.06294283
 568.35867934 732.77017607 736.16626099 743.3919392  747.74854384
 743.58244066 757.04564832]

poly_SVR：The value of default measurement of poly_SVR is 0.5579245050116022
poly_SVR：R-squared value of DecisionTreeRegressor: 0.5579245050116022
poly_SVR：The mean squared error of DecisionTreeRegressor: 23.45012658485137
poly_SVR：The mean absoluate error of DecisionTreeRegressor: 3.1855299673953557
poly_SVR：测试141~153行数据， 
 [-1.03092264e+06 -7.08914443e+06 -4.71673473e+07 -1.43965988e+08
 -7.08197015e+06 -4.62519130e+07 -1.43821606e+08 -1.34024997e+08
 -1.33032109e+08 -1.32825434e+08 -1.32921612e+08 -1.33055776e+08]

rbf_SVR：The value of default measurement of rbf_SVR is 0.48978384736121017
rbf_SVR：R-squared value of DecisionTreeRegressor: 0.48978384736121017
rbf_SVR：The mean squared error of DecisionTreeRegressor: 27.064683522730615
rbf_SVR：The mean absoluate error of DecisionTreeRegressor: 3.248361364253994
rbf_SVR：测试141~153行数据， 
 [0.38231553 0.38231553 0.38231553 0.38231553 0.38231553 0.38231553
 0.38231553 0.38231553 0.38231553 0.38231553 0.38231553 0.38231553]
DT DTR：The value of default measurement of DTR is 0.2302209550962223
DTR：R-squared value of DecisionTreeRegressor: 0.2302209550962223
DTR：The mean squared error of DecisionTreeRegressor: 40.833333333333336
DTR：The mean absoluate error of DecisionTreeRegressor: 4.111111111111111
DTR：测试141~153行数据， 
 [3.89129343 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343
 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343]
RF RFR：The value of default measurement of RFR is 0.6240708685469911
RFR：R-squared value of DecisionTreeRegressor: 0.6240708685469911
RFR：The mean squared error of DecisionTreeRegressor: 19.941358024691358
RFR：The mean absoluate error of DecisionTreeRegressor: 2.9907407407407405
RFR：测试141~153行数据 
 [2.89029058 3.09049115 3.09049115 3.09049115 3.09049115 3.09049115
 3.09049115 2.90206708 3.10226765 3.10226765 3.09049115 3.09049115]
ETR ETR：The value of default measurement of ETR is 0.7190149388336945
ETR：R-squared value of DecisionTreeRegressor: 0.7190149388336945
ETR：The mean squared error of DecisionTreeRegressor: 14.904999999999989
ETR：The mean absoluate error of DecisionTreeRegressor: 2.6666666666666656
ETR：测试141~153行数据 
 [2.51344245 2.54877196 2.54877196 2.54877196 2.54877196 2.54877196
 2.54877196 2.33679488 2.54877196 2.54877196 2.54877196 2.54877196]
GB/GD

SGDR：The value of default measurement of SGDR is 0.4691791802079268
SGDR：R-squared value of DecisionTreeRegressor: 0.4691791802079268
SGDR：The mean squared error of DecisionTreeRegressor: 28.157668902967323
SGDR：The mean absoluate error of DecisionTreeRegressor: 3.693309750338626
SGDR：测试141~153行数据 
 [125.22055498 181.45266125 288.54749558 392.31320313 184.45855476
 292.27252144 396.6089051  407.38658412 406.82100285 407.48230658
 408.14902864 407.91771216]

GBR：The value of default measurement of GBR is 0.6340219593738237
GBR：R-squared value of DecisionTreeRegressor: 0.6340219593738237
GBR：The mean squared error of DecisionTreeRegressor: 19.413497190530663
GBR：The mean absoluate error of DecisionTreeRegressor: 2.792040256427659
GBR：测试141~153行数据 
 [2.79877177 3.34564311 3.34564311 3.41538604 3.48125832 3.40431433
 3.41538604 2.88403709 3.41983671 3.41983671 3.41983671 3.41983671]
LGB [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
LGB：The value of default measurement of LGB is 0.7257085935738725
LGB：R-squared value of DecisionTreeRegressor: 0.6340219593738237
LGB：The mean squared error of DecisionTreeRegressor: 19.413497190530663
LGB：The mean absoluate error of DecisionTreeRegressor: 2.792040256427659
LGB：测试141~153行数据 
 [2.35312797 2.34599463 2.34599463 2.34599463 2.34599463 2.34599463
 2.34599463 2.34599463 2.34599463 2.34599463 2.34599463 2.34599463]

2、第二次输出评估模型性能记录

LiR LiR：The value of default measurement of LiR is 0.5231458055883893
LiR：R-squared value of DecisionTreeRegressor: 0.5231458055883893
LiR：The mean squared error of DecisionTreeRegressor: 25.294980943838745
LiR：The mean absoluate error of DecisionTreeRegressor: 3.608855227697136
kNN

kNNR_uni：The value of default measurement of kNNR_uni is 0.5866024699259604
kNNR_uni：R-squared value of DecisionTreeRegressor: 0.5866024699259604
kNNR_uni：The mean squared error of DecisionTreeRegressor: 21.928888888888892
kNNR_uni：The mean absoluate error of DecisionTreeRegressor: 3.2111111111111117

kNNR_dis：The value of default measurement of kNNR_dis is 0.6601811947182363
kNNR_dis：R-squared value of DecisionTreeRegressor: 0.6601811947182363
kNNR_dis：The mean squared error of DecisionTreeRegressor: 18.02586682616158
kNNR_dis：The mean absoluate error of DecisionTreeRegressor: 2.847311210973169
SVM

linear_SVR：The value of default measurement of linear_SVR is 0.22831988148305604
linear_SVR：R-squared value of DecisionTreeRegressor: 0.22831988148305604
linear_SVR：The mean squared error of DecisionTreeRegressor: 40.93417678062064
linear_SVR：The mean absoluate error of DecisionTreeRegressor: 4.02480324908603

poly_SVR：The value of default measurement of poly_SVR is 0.5579245050116022
poly_SVR：R-squared value of DecisionTreeRegressor: 0.5579245050116022
poly_SVR：The mean squared error of DecisionTreeRegressor: 23.45012658485137
poly_SVR：The mean absoluate error of DecisionTreeRegressor: 3.1855299673953557

rbf_SVR：The value of default measurement of rbf_SVR is 0.48978384736121017
rbf_SVR：R-squared value of DecisionTreeRegressor: 0.48978384736121017
rbf_SVR：The mean squared error of DecisionTreeRegressor: 27.064683522730615
rbf_SVR：The mean absoluate error of DecisionTreeRegressor: 3.248361364253994
DT DTR：The value of default measurement of DTR is -0.3190975606208273
DTR：R-squared value of DecisionTreeRegressor: -0.3190975606208273
DTR：The mean squared error of DecisionTreeRegressor: 69.97222222222223
DTR：The mean absoluate error of DecisionTreeRegressor: 5.027777777777778
RF RFR：The value of default measurement of RFR is 0.6920860546642035
RFR：R-squared value of DecisionTreeRegressor: 0.6920860546642035
RFR：The mean squared error of DecisionTreeRegressor: 16.333456790123456
RFR：The mean absoluate error of DecisionTreeRegressor: 2.861111111111111
ETR ETR：The value of default measurement of ETR is 0.6602917945510349
ETR：R-squared value of DecisionTreeRegressor: 0.6602917945510349
ETR：The mean squared error of DecisionTreeRegressor: 18.019999999999996
ETR：The mean absoluate error of DecisionTreeRegressor: 3.0055555555555555
GB/GD

SGDR：The value of default measurement of SGDR is 0.46348293353800685
SGDR：R-squared value of DecisionTreeRegressor: 0.46348293353800685
SGDR：The mean squared error of DecisionTreeRegressor: 28.459829296344633
SGDR：The mean absoluate error of DecisionTreeRegressor: 3.706383834628725

GBR：The value of default measurement of GBR is 0.6242967693280365
GBR：R-squared value of DecisionTreeRegressor: 0.6242967693280366
GBR：The mean squared error of DecisionTreeRegressor: 19.92937499923262
GBR：The mean absoluate error of DecisionTreeRegressor: 2.8923697398449524
LGB

[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

LGB：The value of default measurement of LGB is 0.7257085935738725
LGB：R-squared value of DecisionTreeRegressor: 0.7257085935738725
LGB：The mean squared error of DecisionTreeRegressor: 14.54993157220446
LGB：The mean absoluate error of DecisionTreeRegressor: 2.9797938407355122
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