ML之回归预测：利用6个单独+2个集成模型(LassoR、KernelRidgeR、ElasticNetR、GBR、XGBR、LGBMR，Avg、Stacking)对自动驾驶数据集【5+1】回归预测

ML之回归预测：利用6个单独+2个集成模型(LassoR、KernelRidgeR、ElasticNetR、GBR、XGBR、LGBMR，Avg、Stacking)对自动驾驶数据集【5+1】进行回归预测

 

目录

利用6个单独+2个集成模型(LassoR、KernelRidgeR、ElasticNetR、GBR、XGBR、LGBMR，Avg、Stacking)对自动驾驶数据集【5+1】进行回归预测

输出结果

实现代码

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利用6个单独+2个集成模型(LassoR、KernelRidgeR、ElasticNetR、GBR、XGBR、LGBMR，Avg、Stacking)对自动驾驶数据集【5+1】进行回归预测

输出结果

kf：  5
LassoR 5f-CV: 0.9303 (0.0088)
kf：  5
LassoR02 5f-CV: 0.9652 (0.0064)
LassoR02，train数据集 0.9661743820406197
LassoR02，test数据集  0.9729422633172817
-------------------------------------
kf：  5
KernelRidgeR 5f-CV: 0.9756 (0.0065)
KernelRidgeR，train数据集 0.9779833615150171
KernelRidgeR，test数据集  0.9735860613699098
-------------------------------------
kf：  5
ElasticNetR 5f-CV: 0.8869 (0.0152)
kf：  5
ElasticNetR02 5f-CV: 0.9652 (0.0064)
ElasticNetR02，train数据集 0.9661740611722313
ElasticNetR02，test数据集  0.9729302413806289
ElasticNetR02，新数据test数据集 0.9012162287120226
-------------------------------------
kf：  5
GBR 5f-CV: 0.9817 (0.0056)
GBR，train数据集 0.9953533672376947
GBR，test数据集  0.9795040374323927
GBR，新数据test数据集 0.9362198547614025
-------------------------------------
kf：  5
XGBR 5f-CV: 0.9798 (0.0049)
XGBR，train数据集 0.9968314776157515
XGBR，test数据集  0.9756711778757111
-------------------------------------
kf：  5
[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319
[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6
[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11
[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319
[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6
[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11
[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319
[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6
[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11
[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319
[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6
[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11
[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319
[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6
[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11
[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
LGBMR 5f-CV: 0.9748 (0.0050)
[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319
[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6
[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11
[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
LGBMR，train数据集 0.9808755176740463
LGBMR，test数据集 0.9692198833898547
LGBMR，新数据test数据集 0.9201215322025592
-------------------------------------
kf：  5
AverageModels：LassoR,ElasticNetR,GBR,对基模型进行集成之后的得分：0.9527 (0.0058)
Avg_models，train数据集 0.9589290712084004
Avg_models，test数据集 0.9491595320322921
Avg_models，新数据test数据集 0.9235325595276671
-------------------------------------
kf：  5
StackingAverageModels：对基模型进行集成之后的得分：0.9728 (0.0052)
stacked_averaged_model，train数据集 0.9840574443555236
stacked_averaged_model，test数据集 0.9727198834799341
stacked_averaged_model，新数据test数据集 0.9585729656542717
采用datetime方法，run time： 0:02:23.166606

 

实现代码

更新……