网格搜索 ValueError：估计器的参数分类器无效

问题描述

我正在尝试将随机森林与网格搜索结合使用，但出现此错误

ValueError: Invalid parameter classifier for estimator Pipeline(steps=[('tfidf_vectorizer',TfidfVectorizer()),('rf_classifier',RandomForestClassifier())]). 
Check the list of available parameters with `estimator.get_params().keys()`.

import numpy as np # linear algebra
import pandas as pd
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn import pipeline,ensemble,preprocessing,feature_extraction,metrics
train=pd.read_json('cleaned_data1')
#split dataset into X,Y
X=train.iloc[:,0]
Y=train.iloc[:,2]

estimators=pipeline.Pipeline([
        ('tfidf_vectorizer',feature_extraction.text.TfidfVectorizer(lowercase=True)),ensemble.RandomForestClassifier())
    ])

print(estimators.get_params().keys())

params = {"classifier__max_depth": [3,None],"classifier__max_features": [1,3,10],"classifier__min_samples_split": [1,"classifier__min_samples_leaf": [1,# "bootstrap": [True,False],"classifier__criterion": ["gini","entropy"]}

X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2)

rf_classifier=GridSearchCV(estimators,params,cv=10,n_jobs=-1,scoring='accuracy',iid=True)

rf_classifier.fit(X_train,y_train)

y_pred=rf_classifier.predict(X_test)

metrics.confusion_matrix(y_test,y_pred)
print(metrics.accuracy_score(y_test,y_pred))

我已经尝试添加这些参数

param_grid = {
    'n_estimators': [200,500],'max_features': ['auto','sqrt','log2'],'max_depth' : [4,5,6,7,8],'criterion' :['gini','entropy']
}

还是一样的错误

解决方法

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