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
【摘要】 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
相关文章ML之回归预测：利用九大类机器学习算法对自动驾驶系统参数(2018年的data,18+2)进行回归预测+评估九种模型性能
目录
输出记录
1、第一次输出错误记录
2、第二次输出评估模型性能记录
输出记录
1、第一...
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
相关文章
ML之回归预测：利用九大类机器学习算法对自动驾驶系统参数(2018年的data,18+2)进行回归预测+评估九种模型性能
目录
输出记录
1、第一次输出错误记录
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
| LiR | LiR：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：测试141~153行数据， [[ 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_dis：The value of default measurement of kNNR_dis is 0.6601811947182363 |
| SVM | linear_SVR：The value of default measurement of linear_SVR is 0.22831988148305604
|
| DT | DTR：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：测试141~153行数据， [3.89129343 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343 3.89129343] |
| RF | RFR：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：测试141~153行数据 [2.89029058 3.09049115 3.09049115 3.09049115 3.09049115 3.09049115 3.09049115 2.90206708 3.10226765 3.10226765 3.09049115 3.09049115] |
| ETR | ETR：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：测试141~153行数据 [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
|
| 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：测试141~153行数据 [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、第二次输出评估模型性能记录
| LiR | LiR：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
|
| SVM | linear_SVR：The value of default measurement of linear_SVR is 0.22831988148305604
|
| DT | DTR：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 |
| RF | RFR：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 |
| ETR | ETR：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
|
| LGB | [LightGBM] [Warning] feature_fraction is set=0.6, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6
|
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