改装TPOT回归器后无法重现分数

如何解决改装TPOT回归器后无法重现分数

tpot 的导出管道说明 Average CV score on the training set was: -128.90187963562252 (neg_MAE)。 然而，使用相同的精确训练集重新拟合管道会产生更小的 MAE（35）。 此外，预测看不见的测试集会产生一个大约 (140) 的 MAE，这与导出的管道说明的一致。

我有点困惑，想知道如何在训练集上重现错误分数。

管道似乎过度拟合了吧？？

cv = RepeatedKFold(n_splits=4,n_repeats=1,random_state=1)
model = tpotRegressor(generations=10,population_size=25,offspring_size=None,mutation_rate=0.9,crossover_rate=0.1,scoring='neg_mean_absolute_error',cv=cv,subsample=0.75,n_jobs=-1,max_time_mins=None,max_eval_time_mins=5,random_state=42,config_dict=None,template=None,warm_start=False,memory=None,use_dask=False,periodic_checkpoint_folder=None,early_stop=3,verbosity=2,disable_update_check=False,log_file=None)

model.fit(train_df[x],train_df[y])

# The Exported model
# Average CV score on the training set was: -128.90187963562252

exported_pipeline = make_pipeline(StackingEstimator(estimator=LassolarsCV(normalize=True)),StackingEstimator(estimator=ExtraTreesRegressor(bootstrap=True,max_features=0.4,min_samples_leaf=1,min_samples_spli`enter code here`t=7,n_estimators=100)),polynomialFeatures(degree=2,include_bias=False,interaction_only=False),ExtraTreesRegressor(bootstrap=True,max_features=0.15000000000000002,min_samples_leaf=9,min_samples_split=7,n_estimators=100))

# Fix random state for all the steps in exported pipeline

set_param_recursive(exported_pipeline.steps,'random_state',42)
exported_pipeline.fit(training_features,training_target) 
results = exported_pipeline.predict(testing_features)

提前致谢