管道中的Hyperparametrs XGBOOST的正确名称是什么？

Question

我正在处理分类问题，并且正在使用Grisearch方法来查找最佳超参数。 但是，我使用管道的体系结构来构建相同的分类模型，仅更改分类器。 xgboost的错误是参数的字符串。 您有什么建议吗？

        elif method == "XGBoost":

            #classifier = Boosting
            classifier = XGBClassifier(random_state = 0, n_jobs = 4)

            parameters = {"xgb__max_depth":[3,4,5,6,7,9],
                          "xgb__gamma":[0, 0.1, 0.2],
                          "xgb__colsample_bytree":[0.5,0.6,0.7,0.8,0.9],                
                          "xgb__n_estimators": [10, 50, 100, 500],
                          "xgb__learning_rate": [0.1, 0.5, 1],
                          'xgb__min_child_weight': [1, 3, 4, 5, 6]

                    }


        print("Start PIPELINE !!!")

        # Add one transformers and two samplers in the pipeline object
        pipeline = make_pipeline(renn, smote_enn, classifier)
        #pipeline = make_pipeline(knn)
        print()
        print(" Starting Grid Search, with this method: " + method)
        print()


    #If it is not clear review the link from Stack
    #https://stackoverflow.com/questions/48370150/how-to-implement-smote-in-cross-validation-and-gridsearchcv

        scorers = {
                'precision_score': make_scorer(precision_score, pos_label="1"),
                'recall_score': make_scorer(recall_score, pos_label="1"),
                'accuracy_score': make_scorer(accuracy_score),
                'f1_scorer': make_scorer(f1_score, pos_label="1")
            }

        random_search = GridSearchCV(pipeline,  param_grid = parameters ,
                                           cv = kf,  scoring = scorers, refit = 'recall_score')
        gg = random_search.fit(X, y)

但是我得到了这个错误：

ValueError: Invalid parameter xgb for estimator Pipeline(memory=None,
steps=[('repeatededitednearestneighbours', RepeatedEditedNearestNeighbours(kind_sel='all', max_iter=100, n_jobs=2,
n_neighbors=5, random_state=0, ratio=None,
return_indices=False, sampling_strategy='auto')), ('smoteenn', SMOTEENN(enn=None, random_state=0, ratio=None, ...
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=True, subsample=1))]). Check the list of available parameters with `estimator.get_params().keys()`.

Answer 1

管道中步骤的名称不是xgb ，而是xgb的类名称（即xgbclassifier ）。 因此，您可以在parameters使用该名称，也可以直接使用Pipeline而不是make_pipeline并根据需要设置步骤名称，例如xgb