集成学习 Python-随机森林、SVM、KNN [英] Ensemble learning Python-Random Forest, SVM, KNN

问题描述

我正在尝试集成分类器随机森林、SVM 和 KNN.在这里，我将 VotingClassifier 与 GridSearchCV 结合使用.如果我尝试使用逻辑回归、随机森林和高斯，代码运行良好

I am trying to ensemble the classifiers Random forest, SVM and KNN. Here to ensemble, I'm using the VotingClassifier with GridSearchCV. The code is working fine if I try with the Logistic regression, Random Forest and Gaussian

clf11 = LogisticRegression(random_state=1)
clf12 = RandomForestClassifier(random_state=1)
clf13 = GaussianNB()

但是我不知道我在下面的代码中做错了什么，因为我是初学者.这是我尝试使用随机森林、KNN 和 SVM

But I don't know what I was wrong in this below code cause I'm a beginner. Here is my try to work with Random forest, KNN and SVM

from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import VotingClassifier

clf11 = RandomForestClassifier(n_estimators=100,criterion="entropy")
clf12 = KNeighborsClassifier(n_neighbors=best_k)
clf13 = SVC(kernel='rbf', probability=True)
eclf1 = VotingClassifier(estimators=[('lr', clf11), ('rf', clf12), ('gnb', clf13)],voting='hard')

params = {'lr__C': [1.0, 100.0], 'rf__n_estimators': [20, 200]}

grid1 = GridSearchCV(estimator=eclf1, param_grid=params, cv=30)
grid1.fit(X_train,y_train)
grid1_predicted = grid1.predict(X_test)
print('Accuracy score : {}%'.format(accuracy_score(y_test,grid1_predicted)*100))
scores_dict['Logistic-Random-Gaussian'] = accuracy_score(y_test,grid1_predicted)*100

每当我运行这个我得到

Invalid parameter estimator VotingClassifier.

这些是我遇到的错误.

是否可以集成随机森林、svm 和 KNN?

或者，有没有其他方法可以做到?

推荐答案

贴出的代码如下:

clf11 = RandomForestClassifier(n_estimators=100,criterion="entropy")
clf12 = KNeighborsClassifier(n_neighbors=best_k)
clf13 = SVC(kernel='rbf', probability=True)
eclf1 = VotingClassifier(estimators=[('lr', clf11), ('rf', clf12), ('gnb', clf13)],voting='hard')

params = {'lr__C': [1.0, 100.0], 'rf__n_estimators': [20, 200]}

在这里，您对 RandomForestClassifier 使用了 hiperparameters C，这将不起作用.

Here, you are using hiperparameters C for RandomForestClassifier, which will not work.

您必须使用对正在使用的分类器有效的 hiperparameters.也许名称lr"，rf"和gnb"对于估计量显示被其他更合适的替代，然后选择对不同类型分类器有效的超参数

You must use hiperparameters that are valid for the classifiers that are being used. Maybe the names "lr", "rf" and "gnb" for the estimators show be replaced by other more adequate and then selecting hiperparameters valid for the different kind of classifiers

以下方法可行:

clf11 = RandomForestClassifier(n_estimators=100,criterion="entropy")
clf12 = KNeighborsClassifier(n_neighbors=best_k)
clf13 = SVC(kernel='rbf', probability=True)
eclf1 = VotingClassifier(estimators=[('rf', clf11), ('knn', clf12), ('svc', clf13)],voting='hard')

params = {'svc__C': [1.0, 100.0], 'rf__n_estimators': [20, 200]}

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