ML之回归预测：利用九大类机器学习算法对无人驾驶汽车系统参数(2018年的data,18+2)进行回归预测值VS真实值

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一个处女座的程序猿 发表于 2021/03/25 23:30:34 2021/03/25
【摘要】 ML之回归预测：利用九大类机器学习算法对无人驾驶汽车系统参数(2018年的data,18+2)进行回归预测值VS真实值 相关文章ML之回归预测：利用九大类机器学习算法对无人驾驶汽车系统参数(2018年的data,18+2)进行回归预测值VS真实值     目录 输出结果 核心代码     输出结果     数据的初步查验：输出回归目标值的差异...

ML之回归预测：利用九大类机器学习算法对无人驾驶汽车系统参数(2018年的data,18+2)进行回归预测值VS真实值

相关文章
ML之回归预测：利用九大类机器学习算法对无人驾驶汽车系统参数(2018年的data,18+2)进行回归预测值VS真实值

 

 

目录

输出结果

核心代码

 

 

输出结果

 

 


  
    
   
  1. 
    
    
     
    
    
    
    
    
     
    
      数据的初步查验：输出回归目标值的差异
     
    
    
    2. 
   
  2. 
    
    
     
    
    
    
    
    
     
    
      The max target value is PeakNonedb 89
     
    
    
    3. 
   
  3. 
    
    
     
    
    
    
    
    
     
    
      dtype: int64
     
    
    
    4. 
   
  4. 
    
    
     
    
    
    
    
    
     
    
      The min target value is PeakNonedb 56
     
    
    
    5. 
   
  5. 
    
    
     
    
    
    
    
    
     
    
      dtype: int64
     
    
    
    6. 
   
  6. 
    
    
     
    
    
    
    
    
     
    
      The average target value is PeakNonedb 63.392157
     
    
    
    7. 
   
  7. 
    
    
     
    
    
    
    
    
     
    
      dtype: float64
     
    
    
    8. 
   
  8. 
    
    
     
    
    
    
    
    
     
    
      X_test进行归一化： 
     
    
    
    9. 
   
  9. 
    
    
     
    
    
    
    
    
     
    
       [[-0.9491207  -1.77209939 -0.79948391 -1.43561411 -1.57260903 -1.40726549
     
    
    
    10. 
   
  10. 
    
    
     
    
    
    
    
    
     
      -1.45642384 -1.48633439 -1.3001131  -1.39201745 -1.43714071 -2.7383659
     
    
    
    11. 
   
  11. 
    
    
     
    
    
    
    
    
     
      -0.94919765 -0.73005097  0. -0.49383335 -0.65675347]
     
    
    
    12. 
   
  12. 
    
    
     
    
    
    
    
    
     
    
       [-0.75177877 -1.77498304 -0.79948391 -1.23709687 -1.34056255 -1.34617547
     
    
    
    13. 
   
  13. 
    
    
     
    
    
    
    
    
     
      -1.2643713  -1.28972931 -1.09785003 -1.17393121 -1.22164307 -2.31661538
     
    
    
    14. 
   
  14. 
    
    
     
    
    
    
    
    
     
      -1.14197474 -1.03125079  0. -0.49383335 -0.64970028]
     
    
    
    15. 
   
  15. 
    
    
     
    
    
    
    
    
     
    
       [-0.55443684 -1.77209939 -0.79948391 -0.86678588 -0.91216904 -0.97963535
     
    
    
    16. 
   
  16. 
    
    
     
    
    
    
    
    
     
      -0.8962706  -0.92109479 -0.64443991 -0.73819491 -0.80934977 -1.47311433
     
    
    
    17. 
   
  17. 
    
    
     
    
    
    
    
    
     
      -1.14197474 -1.3324506   0. -0.50137443 -0.64970028]
     
    
    
    18. 
   
  18. 
    
    
     
    
    
    
    
    
     
    
       [-0.35709492 -1.77498304 -0.79948391 -0.50029252 -0.49270039 -0.50757609
     
    
    
    19. 
   
  19. 
    
    
     
    
    
    
    
    
     
      -0.53350469 -0.55491783 -0.21524754 -0.30289478 -0.41108294 -0.41873802
     
    
    
    20. 
   
  20. 
    
    
     
    
    
    
    
    
     
      -1.33475182 -1.3324506   0. -0.52399769 -0.65675347]
     
    
    
    21. 
   
  21. 
    
    
     
    
    
    
    
    
     
    
       [-0.15975299 -1.48373433 -0.79948391 -1.18746756 -1.28255093 -1.30452318
     
    
    
    22. 
   
  22. 
    
    
     
    
    
    
    
    
     
      -1.21102337 -1.23074779 -1.08349877 -1.17436739 -1.22546848 -2.31661538
     
    
    
    23. 
   
  23. 
    
    
     
    
    
    
    
    
     
      -0.17808931 -0.27825126  0. -0.49383335 -0.62854068]
     
    
    
    24. 
   
  24. 
    
    
     
    
    
    
    
    
     
    
       [ 0.03758894 -1.48373433 -0.79948391 -0.82479185 -0.86308228 -1.01295718
     
    
    
    25. 
   
  25. 
    
    
     
    
    
    
    
    
     
      -0.85625965 -0.86702839 -0.66731223 -0.73863108 -0.80934977 -1.3676767
     
    
    
    26. 
   
  26. 
    
    
     
    
    
    
    
    
     
      -0.94919765 -0.88065088  0. -0.49383335 -0.64264708]
     
    
    
    27. 
   
  27. 
    
    
     
    
    
    
    
    
     
    
       [ 0.23493087 -1.48373433 -0.79948391 -0.48502196 -0.47485066 -0.46592381
     
    
    
    28. 
   
  28. 
    
    
     
    
    
    
    
    
     
      -0.51483291 -0.51313925 -0.22690794 -0.30333095 -0.40938276 -0.20786276
     
    
    
    29. 
   
  29. 
    
    
     
    
    
    
    
    
     
      -1.14197474 -1.18185069  0. -0.49383335 -0.62854068]
     
    
    
    30. 
   
  30. 
    
    
     
    
    
    
    
    
     
    
       [-0.9491207  -0.04479271  0.07932705 -0.58809822 -0.5774866  -0.51312973
     
    
    
    31. 
   
  31. 
    
    
     
    
    
    
    
    
     
      -0.58952001 -0.63847499 -0.2179384  -0.30289478 -0.40640745 -0.20786276
     
    
    
    32. 
   
  32. 
    
    
     
    
    
    
    
    
     
      -1.52752891 -1.78425032  0. -0.48629226 -0.61443429]
     
    
    
    33. 
   
  33. 
    
    
     
    
    
    
    
    
     
    
       [-0.75177877 -0.04190905  0.07932705 -0.59191586 -0.58641147 -0.52979065
     
    
    
    34. 
   
  34. 
    
    
     
    
    
    
    
    
     
      -0.59218741 -0.62127205 -0.21659297 -0.30333095 -0.40895772 -0.41873802
     
    
    
    35. 
   
  35. 
    
    
     
    
    
    
    
    
     
      -1.52752891 -1.63365041  0. -0.49383335 -0.62854068]
     
    
    
    36. 
   
  36. 
    
    
     
    
    
    
    
    
     
    
       [-0.55443684 -0.04190905  0.07932705 -0.59191586 -0.58641147 -0.5381211
     
    
    
    37. 
   
  37. 
    
    
     
    
    
    
    
    
     
      -0.5975222  -0.60652667 -0.22466556 -0.30333095 -0.40768258 -0.41873802
     
    
    
    38. 
   
  38. 
    
    
     
    
    
    
    
    
     
      -1.52752891 -1.63365041  0. -0.49383335 -0.62148748]
     
    
    
    39. 
   
  39. 
    
    
     
    
    
    
    
    
     
    
       [-0.35709492 -0.04479271  0.07932705 -0.54610419 -0.51501255 -0.48536154
     
    
    
    40. 
   
  40. 
    
    
     
    
    
    
    
    
     
      -0.55484386 -0.55000271 -0.22825337 -0.30333095 -0.40853267 -0.31330039
     
    
    
    41. 
   
  41. 
    
    
     
    
    
    
    
    
     
      -1.33475182 -1.3324506   0. -0.49383335 -0.62148748]
     
    
    
    42. 
   
  42. 
    
    
     
    
    
    
    
    
     
    
       [-0.15975299 -0.04767636  0.07932705 -0.50411016 -0.47038823 -0.51312973
     
    
    
    43. 
   
  43. 
    
    
     
    
    
    
    
    
     
      -0.51216551 -0.5573754  -0.20941734 -0.30333095 -0.41405825 -0.20786276
     
    
    
    44. 
   
  44. 
    
    
     
    
    
    
    
    
     
      -1.14197474 -1.3324506   0. -0.48629226 -0.62148748]]
     
    
    
    45.

各个模型结果

LiR LiR：The value of default measurement of LiR is 0.5231458055883889
LiR：R-squared value of DecisionTreeRegressor: 0.5231458055883889
LiR：测试141~153行数据， 
 [[56.63220089]
 [58.94184439]
 [59.10056518]
 [56.54114422]
 [60.11923295]
 [60.81269213]
 [57.55507446]
 [61.38670841]
 [61.58889402]
 [61.77824699]
 [61.18940628]
 [62.06650565]]
kNN

kNNR_uni：The value of default measurement of kNNR_uni is 0.5866024699259602
kNNR_uni：R-squared value of DecisionTreeRegressor: 0.5866024699259602
kNNR_uni：测试141~153行数据， 
 [[59.4]
 [59.4]
 [59. ]
 [58.4]
 [60. ]
 [59.2]
 [58.4]
 [64. ]
 [64. ]
 [63.4]
 [64. ]
 [62.4]]

 

kNNR_dis：The value of default measurement of kNNR_dis is 0.6601811947182363
kNNR_dis：R-squared value of DecisionTreeRegressor: 0.6601811947182363
kNNR_dis：测试141~153行数据， 
 [[59.45759031]
 [59.42810453]
 [58.6914726 ]
 [58.22296918]
 [59.88108538]
 [59.00540794]
 [58.31774397]
 [64.07716708]
 [64.06438322]
 [63.6972427 ]
 [63.99225839]
 [62.52719181]]
SVM

linear_SVR：The value of default measurement of linear_SVR is 0.1743724332386528
linear_SVR：R-squared value of DecisionTreeRegressor: 0.1743724332386528
linear_SVR：测试141~153行数据， 
 [59.55435497 59.69256353 59.48594623 58.7893093  59.85519382 59.63084943
 58.78629636 60.93419466 61.09773299 61.11689926 60.99255187 61.03568282]


poly_SVR：The value of default measurement of poly_SVR is 0.23998631177335328
poly_SVR：R-squared value of DecisionTreeRegressor: 0.23998631177335328
poly_SVR：测试141~153行数据， 
 [58.88092402 59.14921323 59.66463047 60.04552501 59.70864622 59.94978874
 60.25534603 60.45124799 60.52748458 60.57400671 60.58719271 60.58516079]


rbf_SVR：The value of default measurement of rbf_SVR is 0.04812627724989971
rbf_SVR：R-squared value of DecisionTreeRegressor: 0.04812627724989971
rbf_SVR：测试141~153行数据， 
 [60.1701123  60.06532389 60.0374718  60.18953708 60.12628982 59.94386044
 60.04551404 61.1878686  61.04114038 60.99813177 60.77741395 60.6847975 ]
DT DTR：The value of default measurement of DTR is 0.4428265960696466
DTR：R-squared value of DecisionTreeRegressor: 0.4428265960696466
DTR：测试141~153行数据， 
 [60. 58. 62. 64. 58. 62. 56. 65. 64. 57. 56. 64.]
RF RFR：The value of default measurement of RFR is 0.7295335069166653
RFR：R-squared value of DecisionTreeRegressor: 0.7295335069166653
RFR：测试141~153行数据 
 [59.2        60.53333333 60.26666667 62.46666667 60.2        59.86666667
 59.8        64.46666667 64.33333333 61.6        60.33333333 63.2       ]
ETR ETR：The value of default measurement of ETR is 0.762766666181797
ETR：R-squared value of DecisionTreeRegressor: 0.762766666181797
ETR：测试141~153行数据 
 [59.1 59.3 59.2 60.3 61.1 59.1 59.7 63.5 63.  62.8 61.8 62.2]
GB/GD

SGDR：The value of default measurement of SGDR is -4.233646688626224
SGDR：R-squared value of DecisionTreeRegressor: -4.233646688626224
SGDR：测试141~153行数据 
 [46.0143165  45.57959739 44.68507644 43.85567515 46.40920755 45.41266318
 44.09993622 45.84313973 46.28048918 46.31355335 46.49699207 46.34137258]


GBR：The value of default measurement of GBR is 0.635820912452143
GBR：R-squared value of DecisionTreeRegressor: 0.635820912452143
GBR：测试141~153行数据 
 [58.24846635 58.03767496 58.93168378 62.60456119 58.52581388 58.46728317
 59.64879765 64.43689168 62.95990227 59.72812037 60.00259156 63.0985046 ]
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 0.7127670905333733
LGB：R-squared value of DecisionTreeRegressor: 0.7127670905333733
LGB：测试141~153行数据 
 [59.59918175 59.59918175 59.00311914 61.18846478 56.78311139 60.21894816
 58.94408217 62.11993132 63.35024497 60.31777442 59.62890793 61.96502674]

 

 

核心代码

后期更新……

 

 

 

 

 

文章来源: yunyaniu.blog.csdn.net，作者：一个处女座的程序猿，版权归原作者所有，如需转载，请联系作者。

原文链接：yunyaniu.blog.csdn.net/article/details/88637383

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