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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

發(fā)布時(shí)間：2025/3/21 编程问答 21 豆豆

生活随笔收集整理的這篇文章主要介紹了 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真实值小編覺(jué)得挺不錯(cuò)的,現(xiàn)在分享給大家,幫大家做個(gè)參考.

ML之回歸預(yù)測(cè)：利用八(9-1)種機(jī)器學(xué)習(xí)算法對(duì)無(wú)人駕駛汽車(chē)參數(shù)(2017年的data,18+2)進(jìn)行回歸預(yù)測(cè)值VS真實(shí)值

模型評(píng)估

輸出結(jié)果

模型評(píng)估

相關(guān)文章：ML之回歸預(yù)測(cè)：利用八(9-1)種機(jī)器學(xué)習(xí)算法對(duì)無(wú)人駕駛汽車(chē)系統(tǒng)參數(shù)(2017年的data,18+2)進(jìn)行回歸預(yù)測(cè)+評(píng)估八種模型性能

輸出結(jié)果

1、13.0環(huán)境下

1.1、輸出預(yù)測(cè)值

1.2、模型性能評(píng)估及輸出預(yù)測(cè)值

各個(gè)模型結(jié)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：測(cè)試1131~1138行數(shù)據(jù)，? ?[[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：測(cè)試1131~1138行數(shù)據(jù)，? ?[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：測(cè)試1131~1138行數(shù)據(jù)，? ?[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：測(cè)試1131~1138行數(shù)據(jù)，? ?[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：測(cè)試1131~1138行數(shù)據(jù)，? ?[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：測(cè)試1131~1138行數(shù)據(jù)，?	?
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：測(cè)試1131~1138行數(shù)據(jù)，? ?[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：測(cè)試1131~1138行數(shù)據(jù)，? ?[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：測(cè)試1131~1138行數(shù)據(jù)，? ?[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：測(cè)試1131~1138行數(shù)據(jù)，? ?[1.3993656 ?0.91062936 1.22062928 1.34866033 1.06943559 1.11018125]	?

2、在【12.9,13.0】環(huán)境下

2.1、輸出預(yù)測(cè)值

3、在【12.8,13.0】環(huán)境下

3.1、輸出預(yù)測(cè)值

總結(jié)

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