ML之回归预测:利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测+评估八种模型性能
目录
说明
输出结果
说明
在 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(18+2)进行回归预测值VS真实值
基础上出现了两个bug,成功解决。
(1)、成功解决TypeError: unhashable type: 'numpy.ndarray'
(2)、成功解决TypeError: unsupported operand type(s) for %: 'NoneType' and 'dict'
输出结果
1、在【12.9, 13.0】环境下
![ML之回归预测:利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测+评估八种模型性能 ML之回归预测:利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测+评估八种模型性能](/default/index/img?u=aHR0cDovL3d3dy5pY29kZTkuY29tL2kvbGkvP249NCZpPWltYWdlcy9ibG9nLzIwMjEwNi8xNS9jMjYyNDdiNmJjNjZkZmI2NjQ5ZmEzMDQ5NzE5YWM3Ni5wbmc/LHNpemVfMTYsdGV4dF9RRFV4UTFSUDVZMmE1YTZpLGNvbG9yX0ZGRkZGRix0XzEwMCxnX3NlLHhfMTAseV8xMCxzaGFkb3dfOTAsdHlwZV9abUZ1WjNwb1pXNW5hR1ZwZEdrPQ==)
![ML之回归预测:利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测+评估八种模型性能 ML之回归预测:利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测+评估八种模型性能](/default/index/img?u=aHR0cDovL3d3dy5pY29kZTkuY29tL2kvbGkvP249NCZpPWltYWdlcy9ibG9nLzIwMjEwNi8xNS8yOThiZDcwNDViYTg2M2I2YTVmZDc2MDRkMmY4ZGEzYy5wbmc/LHNpemVfMTYsdGV4dF9RRFV4UTFSUDVZMmE1YTZpLGNvbG9yX0ZGRkZGRix0XzEwMCxnX3NlLHhfMTAseV8xMCxzaGFkb3dfOTAsdHlwZV9abUZ1WjNwb1pXNW5hR1ZwZEdrPQ==)
1.2、模型性能评估及输出预测值
各个模型结果
LiR |
LiR:The value of default measurement of LiR is 0.4125342966025278 LiR:R-squared value of DecisionTreeRegressor: 0.41253429660252783 LiR:The mean squared error of DecisionTreeRegressor: 5.687204916076843 LiR:The mean absoluate error of DecisionTreeRegressor: 1.688779184910588 LiR:测试1131~1138行数据, [[0.39260249] [0.56158086] [0.66445704] [0.75795626] [0.83294215] [0.84325901]] |
|
SVM |
linear_SVR:The value of default measurement of linear_SVR is 0.5024128304336872 linear_SVR:R-squared value of DecisionTreeRegressor: 0.5024128304336872 linear_SVR:The mean squared error of DecisionTreeRegressor: 4.817098565189997 linear_SVR:The mean absoluate error of DecisionTreeRegressor: 1.4822824851546261 linear_SVR:测试1131~1138行数据, [0.68489265 0.8230609 0.88380302 0.95656835 0.98611563 1.02264102] poly_SVR:The value of default measurement of poly_SVR is 0.5371358572097877 poly_SVR:R-squared value of DecisionTreeRegressor: 0.5371358572097877 poly_SVR:The mean squared error of DecisionTreeRegressor: 4.4809479313061065 poly_SVR:The mean absoluate error of DecisionTreeRegressor: 1.1042932962440708 poly_SVR:测试1131~1138行数据, [0.74006387 0.99232855 1.02709907 1.04999397 1.01658734 0.99276056] rbf_SVR:The value of default measurement of rbf_SVR is 0.7419598320911289 rbf_SVR:R-squared value of DecisionTreeRegressor: 0.7419598320911289 rbf_SVR:The mean squared error of DecisionTreeRegressor: 2.4980646580549646 rbf_SVR:The mean absoluate error of DecisionTreeRegressor: 1.0023906945802386 rbf_SVR:测试1131~1138行数据, [0.87034053 0.94602667 0.9724284 1.01138968 1.00514582 1.03902654] |
|
DT |
DTR:The value of default measurement of DTR is -0.034791814149233824 DTR:R-squared value of DecisionTreeRegressor: -0.034791814149233824 DTR:The mean squared error of DecisionTreeRegressor: 10.0177304964539 DTR:The mean absoluate error of DecisionTreeRegressor: 1.4078014184397163 DTR:测试1131~1138行数据, [1.44129906 1.1913833 1.1913833 1.1913833 1.1913833 0.94146754] |
|
RF |
RFR:The value of default measurement of RFR is 0.7143901333350653 RFR:R-squared value of DecisionTreeRegressor: 0.7143901333350653 RFR:The mean squared error of DecisionTreeRegressor: 2.7649645390070923 RFR:The mean absoluate error of DecisionTreeRegressor: 1.0191489361702128 RFR:测试1131~1138行数据, |
|
ETR |
ETR:The value of default measurement of ETR is 0.7895434913913477 ETR:R-squared value of DecisionTreeRegressor: 0.7895434913913478 ETR:The mean squared error of DecisionTreeRegressor: 2.0374113475177302 ETR:The mean absoluate error of DecisionTreeRegressor: 0.9790780141843972 ETR:测试1131~1138行数据, [1.29134961 1.01644227 1.04143384 1.16639172 1.14140015 1.09141699] |
|
GB/GD |
SGDR:The value of default measurement of SGDR is 0.28663918777885733 SGDR:R-squared value of DecisionTreeRegressor: 0.28663918777885733 SGDR:The mean squared error of DecisionTreeRegressor: 6.905984629805215 SGDR:The mean absoluate error of DecisionTreeRegressor: 1.8298880068703798 SGDR:测试1131~1138行数据, [0.72109893 0.74773439 0.75200051 0.74284389 0.74950052 0.71633365] GBR:The value of default measurement of GBR is 0.33837779185765615 GBR:R-squared value of DecisionTreeRegressor: 0.33837779185765615 GBR:The mean squared error of DecisionTreeRegressor: 6.405107656449695 GBR:The mean absoluate error of DecisionTreeRegressor: 1.0884549292443049 GBR:测试1131~1138行数据, [1.26085339 1.24070607 1.17201814 1.20110767 1.23182112 1.24516423] |
|
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 0.7889733551704177 LGB:R-squared value of DecisionTreeRegressor: 0.7889733551704177 LGB:The mean squared error of DecisionTreeRegressor: 2.042930787205453 LGB:The mean absoluate error of DecisionTreeRegressor: 1.0168020659984283 LGB:测试1131~1138行数据, [1.3993656 0.91062936 1.22062928 1.34866033 1.06943559 1.11018125] |
|
2、在【12.8,13.0】环境下
![ML之回归预测:利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测+评估八种模型性能 ML之回归预测:利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测+评估八种模型性能](/default/index/img?u=aHR0cDovL3d3dy5pY29kZTkuY29tL2kvbGkvP249NCZpPWltYWdlcy9ibG9nLzIwMjEwNi8xNS9iMWI1NjU5OGZhMTc0MWZkMGI4ZDQ4MmM2NDM4MTE3Yy5wbmc/LHNpemVfMTYsdGV4dF9RRFV4UTFSUDVZMmE1YTZpLGNvbG9yX0ZGRkZGRix0XzEwMCxnX3NlLHhfMTAseV8xMCxzaGFkb3dfOTAsdHlwZV9abUZ1WjNwb1pXNW5hR1ZwZEdrPQ==)
![ML之回归预测:利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测+评估八种模型性能 ML之回归预测:利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测+评估八种模型性能](/default/index/img?u=aHR0cDovL3d3dy5pY29kZTkuY29tL2kvbGkvP249NCZpPWltYWdlcy9ibG9nLzIwMjEwNi8xNS9jNmUxMjQxNTBiMzZlYmYzZGU5YzQwMTBkNjZjYWM4Yy5wbmc/LHNpemVfMTYsdGV4dF9RRFV4UTFSUDVZMmE1YTZpLGNvbG9yX0ZGRkZGRix0XzEwMCxnX3NlLHhfMTAseV8xMCxzaGFkb3dfOTAsdHlwZV9abUZ1WjNwb1pXNW5hR1ZwZEdrPQ==)