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之回歸預(yù)測(cè)：利用九大類機(jī)器學(xué)習(xí)算法對(duì)無人駕駛汽車系統(tǒng)參數(shù)(2018年的data,18+2)進(jìn)行回歸預(yù)測(cè)值VS真實(shí)值
相關(guān)文章
ML之回歸預(yù)測(cè)：利用九大類機(jī)器學(xué)習(xí)算法對(duì)無人駕駛汽車系統(tǒng)參數(shù)(2018年的data,18+2)進(jìn)行回歸預(yù)測(cè)值VS真實(shí)值
?
?
目錄
輸出結(jié)果
核心代碼
?
?
輸出結(jié)果
?
?
數(shù)據(jù)的初步查驗(yàn)：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 X_test進(jìn)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(gè)模型結(jié)果
| LiR | LiR：The value of default measurement of LiR is 0.5231458055883889 LiR：R-squared value of DecisionTreeRegressor: 0.5231458055883889 LiR：測(cè)試141~153行數(shù)據(jù)，? ?[[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_dis：The value of default measurement of kNNR_dis is 0.6601811947182363 |
| SVM | linear_SVR：The value of default measurement of linear_SVR is 0.1743724332386528
|
| DT | DTR：The value of default measurement of DTR is 0.4428265960696466 DTR：R-squared value of DecisionTreeRegressor: 0.4428265960696466 DTR：測(cè)試141~153行數(shù)據(jù)，? ?[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(cè)試141~153行數(shù)據(jù)? ?[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(cè)試141~153行數(shù)據(jù)? ?[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
|
| LGB | [LightGBM] [Warning] feature_fraction is set=0.6, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6
|
?
?
核心代碼
后期更新……
?
?
?
?
?
總結(jié)
以上是生活随笔為你收集整理的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?jī)?nèi)容，希望文章能夠幫你解決所遇到的問題。
- 上一篇： ML之回归预测：利用九大类机器学习算法对
- 下一篇： Windows：利用各种小工具查看自己电