ML之回歸預測：利用九大類機器學習算法對無人駕駛汽車系統參數(2018年的data,18+2)進行回歸預測值VS真實值

目錄

輸出結果

數據的初步查驗：輸出回歸目標值的差異 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 X_test進行歸一化： [[-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]]

各個模型結果

LiR

LiR：The value of default measurement of LiR is 0.5231458055883889

LiR：R-squared value of DecisionTreeRegressor: 0.5231458055883889

LiR：測試141~153行數據，

[[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：測試141~153行數據，

[[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：測試141~153行數據，

[[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：測試141~153行數據，

[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：測試141~153行數據，

[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：測試141~153行數據，

[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：測試141~153行數據，

[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：測試141~153行數據

[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：測試141~153行數據

[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：測試141~153行數據

[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：測試141~153行數據

[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：測試141~153行數據

[59.59918175 59.59918175 59.00311914 61.18846478 56.78311139 60.21894816

58.94408217 62.11993132 63.35024497 60.31777442 59.62890793 61.96502674]

核心代碼

后期更新……

與50位技術專家面對面20年技術見證，附贈技術全景圖

總結

以上是生活随笔為你收集整理的java ML回归预测_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...的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。