Python 如何修复估计器多输出分类器的无效参数最小样本分割

尝试使用网格搜索运行机器学习管道时，我遇到以下错误。我不确定这个错误是从哪里来的，因为网格搜索似乎命名正确，参数正确

"ValueError: Invalid parameter min_samples_split for estimator MultiOutputClassifier(estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False),
           n_jobs=None). Check the list of available parameters with `estimator.get_params().keys()`.
"




model = Pipeline([
        ('features', FeatureUnion([

            ('text_pipeline', Pipeline([
                ('vect', CountVectorizer(tokenizer=tokenize)),
                ('tfidf', TfidfTransformer())
            ])),

            ('starting_verb', StartingVerbExtractor())
        ])),

        ('clf', MultiOutputClassifier(RandomForestClassifier()))
    ])

    parameters = {
        'features__text_pipeline__vect__ngram_range': ((1, 1), (1, 2)),
        'features__text_pipeline__vect__max_df': (0.5, 0.75, 1.0),
        'features__text_pipeline__vect__max_features': (None, 5000, 10000),
        'clf__n_estimators': [50, 100, 200],
        'clf__min_samples_split': [2, 3, 4]
    }


    cv = GridSearchCV(model, param_grid=parameters, verbose=2, n_jobs=4)

---

找到了错误的罪魁祸首。我将参数更改为以下内容：

parameters = {
        'features__text_pipeline__vect__ngram_range': ((1, 1), (1, 2)),
        'features__text_pipeline__vect__max_df': (0.5, 0.75, 1.0),
        'features__text_pipeline__vect__max_features': (None, 5000, 10000),
        'clf__estimator__n_estimators': [50, 100, 200],
        'clf__estimator__min_samples_split': [2, 3, 4]
    }

---

找到了错误的罪魁祸首。我将参数更改为以下内容：

parameters = {
        'features__text_pipeline__vect__ngram_range': ((1, 1), (1, 2)),
        'features__text_pipeline__vect__max_df': (0.5, 0.75, 1.0),
        'features__text_pipeline__vect__max_features': (None, 5000, 10000),
        'clf__estimator__n_estimators': [50, 100, 200],
        'clf__estimator__min_samples_split': [2, 3, 4]
    }