我在尝试使用随机森林进行网格搜索时出现了这个错误
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 # 线性代数import pandas as pdfrom sklearn.model_selection import GridSearchCVfrom sklearn.model_selection import train_test_splitfrom sklearn import pipeline,ensemble,preprocessing,feature_extraction,metricstrain=pd.read_json('cleaned_data1')#将数据集分割为 X , YX=train.iloc[:,0]Y=train.iloc[:,2]estimators=pipeline.Pipeline([ ('tfidf_vectorizer', feature_extraction.text.TfidfVectorizer(lowercase=True)), ('rf_classifier', ensemble.RandomForestClassifier()) ])print(estimators.get_params().keys())params = {"classifier__max_depth": [3, None], "classifier__max_features": [1, 3, 10], "classifier__min_samples_split": [1, 3, 10], "classifier__min_samples_leaf": [1, 3, 10], # "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']}但仍然出现同样的错误
回答：
请确保在引用管道中的元素时，使用与初始化参数网格时相同的命名约定。
import numpy as npimport matplotlib.pyplot as pltimport pandas as pdfrom sklearn import datasetsfrom sklearn.decomposition import PCAfrom sklearn.linear_model import LogisticRegressionfrom sklearn.pipeline import Pipelinefrom sklearn.model_selection import GridSearchCV# 定义一个管道来搜索PCA截断和分类器正则化的最佳组合pca = PCA()# 将容忍度设置为较大的值以使示例运行得更快logistic = LogisticRegression(max_iter=10000, tol=0.1)pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])X_digits, y_digits = datasets.load_digits(return_X_y=True)# 可以使用‘__’分隔的参数名称来设置管道的参数:param_grid = { 'pca__n_components': [5, 15, 30, 45, 64], 'logistic__C': np.logspace(-4, 4, 4),}search = GridSearchCV(pipe, param_grid, n_jobs=-1)search.fit(X_digits, y_digits)print("Best parameter (CV score=%0.3f):" % search.best_score_)print(search.best_params_)在这个例子中，我们将LogisticRegression模型称为’logistic’。另外，请注意，对于RandomForestClassifiers，min_samples_split的值不能为1，这会导致错误。
