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)进行回归预测值VS真实值
目录
模型评估
输出结果
模型评估
相关文章：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系统参数(2017年的data,18+2)进行回归预测+评估八种模型性能
输出结果
1、13.0环境下
1.1、输出预测值
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【12.9,13.0】环境下
2.1、 输出预测值
3、在【12.8,13.0】环境下
3.1、 输出预测值
