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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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生活随笔 收集整理的這篇文章主要介紹了 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 小編覺得挺不錯的,現(xiàn)在分享給大家,幫大家做個參考.

ML之回歸預(yù)測:利用九大類機器學(xué)習(xí)算法對無人駕駛系統(tǒng)參數(shù)(2018年的data,18+2)進行回歸預(yù)測+評估九種模型性能

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ML之回歸預(yù)測:利用九大類機器學(xué)習(xí)算法對自動駕駛系統(tǒng)參數(shù)(2018年的data,18+2)進行回歸預(yù)測+評估九種模型性能

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目錄

輸出記錄

1、第一次輸出錯誤記錄

2、第二次輸出評估模型性能記錄



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輸出記錄

1、第一次輸出錯誤記錄

數(shù)據(jù)的初步查驗:輸出回歸目標(biāo)值的差異
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

LiRLiR: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:測試141~153行數(shù)據(jù),?
?[[ 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:測試141~153行數(shù)據(jù),?
?[[0.64097829]
?[0.64097829]
?[0.64097829]
?[0.64097829]
?[0.64097829]
?[0.64097829]
?[0.64097829]
?[0.64097829]
?[0.64097829]
?[0.64097829]
?[0.64097829]
?[0.64097829]]

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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:測試141~153行數(shù)據(jù),?
?[[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:測試141~153行數(shù)據(jù),?
?[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:測試141~153行數(shù)據(jù),?
?[-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:測試141~153行數(shù)據(jù),?
?[0.38231553 0.38231553 0.38231553 0.38231553 0.38231553 0.38231553
?0.38231553 0.38231553 0.38231553 0.38231553 0.38231553 0.38231553]

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DTDTR: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:測試141~153行數(shù)據(jù),?
?[3.89129343 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343
?3.89129343 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343]
RFRFR: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:測試141~153行數(shù)據(jù)?
?[2.89029058 3.09049115 3.09049115 3.09049115 3.09049115 3.09049115
?3.09049115 2.90206708 3.10226765 3.10226765 3.09049115 3.09049115]
ETRETR: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:測試141~153行數(shù)據(jù)?
?[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:測試141~153行數(shù)據(jù)?
?[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:測試141~153行數(shù)據(jù)?
?[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:測試141~153行數(shù)據(jù)?
?[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、第二次輸出評估模型性能記錄

LiRLiR: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

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DTDTR: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
RFRFR: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
ETRETR: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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