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

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
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
X_test进行归一化： 
 [[-0.9491207  -1.77209939 -0.79948391 -1.43561411 -1.57260903 -1.40726549
  -1.45642384 -1.48633439 -1.3001131  -1.39201745 -1.43714071 -2.7383659
  -0.94919765 -0.73005097  0.         -0.49383335 -0.65675347]
 [-0.75177877 -1.77498304 -0.79948391 -1.23709687 -1.34056255 -1.34617547
  -1.2643713  -1.28972931 -1.09785003 -1.17393121 -1.22164307 -2.31661538
  -1.14197474 -1.03125079  0.         -0.49383335 -0.64970028]
 [-0.55443684 -1.77209939 -0.79948391 -0.86678588 -0.91216904 -0.97963535
  -0.8962706  -0.92109479 -0.64443991 -0.73819491 -0.80934977 -1.47311433
  -1.14197474 -1.3324506   0.         -0.50137443 -0.64970028]
 [-0.35709492 -1.77498304 -0.79948391 -0.50029252 -0.49270039 -0.50757609
  -0.53350469 -0.55491783 -0.21524754 -0.30289478 -0.41108294 -0.41873802
  -1.33475182 -1.3324506   0.         -0.52399769 -0.65675347]
 [-0.15975299 -1.48373433 -0.79948391 -1.18746756 -1.28255093 -1.30452318
  -1.21102337 -1.23074779 -1.08349877 -1.17436739 -1.22546848 -2.31661538
  -0.17808931 -0.27825126  0.         -0.49383335 -0.62854068]
 [ 0.03758894 -1.48373433 -0.79948391 -0.82479185 -0.86308228 -1.01295718
  -0.85625965 -0.86702839 -0.66731223 -0.73863108 -0.80934977 -1.3676767
  -0.94919765 -0.88065088  0.         -0.49383335 -0.64264708]
 [ 0.23493087 -1.48373433 -0.79948391 -0.48502196 -0.47485066 -0.46592381
  -0.51483291 -0.51313925 -0.22690794 -0.30333095 -0.40938276 -0.20786276
  -1.14197474 -1.18185069  0.         -0.49383335 -0.62854068]
 [-0.9491207  -0.04479271  0.07932705 -0.58809822 -0.5774866  -0.51312973
  -0.58952001 -0.63847499 -0.2179384  -0.30289478 -0.40640745 -0.20786276
  -1.52752891 -1.78425032  0.         -0.48629226 -0.61443429]
 [-0.75177877 -0.04190905  0.07932705 -0.59191586 -0.58641147 -0.52979065
  -0.59218741 -0.62127205 -0.21659297 -0.30333095 -0.40895772 -0.41873802
  -1.52752891 -1.63365041  0.         -0.49383335 -0.62854068]
 [-0.55443684 -0.04190905  0.07932705 -0.59191586 -0.58641147 -0.5381211
  -0.5975222  -0.60652667 -0.22466556 -0.30333095 -0.40768258 -0.41873802
  -1.52752891 -1.63365041  0.         -0.49383335 -0.62148748]
 [-0.35709492 -0.04479271  0.07932705 -0.54610419 -0.51501255 -0.48536154
  -0.55484386 -0.55000271 -0.22825337 -0.30333095 -0.40853267 -0.31330039
  -1.33475182 -1.3324506   0.         -0.49383335 -0.62148748]
 [-0.15975299 -0.04767636  0.07932705 -0.50411016 -0.47038823 -0.51312973
  -0.51216551 -0.5573754  -0.20941734 -0.30333095 -0.41405825 -0.20786276
  -1.14197474 -1.3324506   0.         -0.48629226 -0.62148748]]

各个模型结果

LiRLiR：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 ]

DTDTR：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.]RFRFR：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       ]ETRETR：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……