使用感知器sklearn.ensemble.AdaBoostClassifier()给出了一个错误

Question

我在AdaBoost分类器中使用感知器存在问题。

在最后一列(“扑克手”)中，将这里的训练和测试数据转换为0和1(原为1~9)，然后在数据中实现总共15个弱感知器的决策树分类器和AdaBoost分类器。我尝试使用scikit-学习库，但是当我的决策树分类器提供了很好的结果时，AdaBoost分类器抛出了错误：

ValueError: BaseClassifier in AdaBoostClassifier ensemble is worse than random, ensemble can not be fit.

在这里，代码的关键部分。

import pandas as pd
from sklearn.ensemble import AdaBoostClassifier
from sklearn.linear_model import Perceptron
from sklearn import metrics

if __name__ == "__main__":
   
    data_train = pd.read_csv("poker-hand-testing.data",header=None)
    data_test = pd.read_csv("poker-hand-training-true.data",header=None)
    

    for value in range(0, len(data_train)):
        if data_train[10][value] != 0:
            data_train[10][value] = 1
    
    for value in range(0, len(data_test)):
        if data_test[10][value] != 0:
            data_test[10][value] = 1

    col=['Suit of card #1','Rank of card #1',
     'Suit of card #2','Rank of card #2',
     'Suit of card #3','Rank of card #3',
     'Suit of card #4','Rank of card #4',
     'Suit of card #5','Rank of card #5',
     'Poker Hand']
    
    data_train.columns=col
    data_test.columns=col
    
    y_train=data_train['Poker Hand']
    y_test=data_test['Poker Hand']
    
    x_train=data_train.drop('Poker Hand',axis=1)
    x_test=data_test.drop('Poker Hand',axis=1)
    
#The problematic part
    classifier = AdaBoostClassifier(base_estimator=Perceptron(), n_estimators=15, algorithm='SAMME')
    classifier = classifier.fit(x_train, y_train)
    y_pred = classifier.predict(x_test)
    
    print("Accuracy of AdaBoost:", metrics.accuracy_score(y_test, y_pred))

奇怪的是，当我不将值更改为二进制值时，此错误每9-10次只发生一次，而二进制值几乎总是会出现错误。此外，将Perceptron()更改为SGDClassifier(loss="perceptron", eta0=1, learning_rate="constant", penalty=None)也会引发此类错误。

我的问题是：

1. 什么是使用的解决方案-学习库？
2. 有没有办法处理这样的例外呢？例如，如果它给出错误，再次执行它，直到想要的结果？
3. 如果不能在scikit-learn库中解决，我还可以在Perceptron中同时使用决策树和AdaBoost吗？

Answer 1

问题部分可以通过尝试-捕捉块来解决。例如,

#The problematic part solution
AdaBoost_accuracy = 0

while AdaBoost_accuracy == 0:
    try:
        classifier = AdaBoostClassifier(base_estimator=Perceptron(), n_estimators=15, algorithm='SAMME')
        classifier = classifier.fit(x_train, y_train)
        y_pred = classifier.predict(x_test)
        AdaBoost_accuracy = metrics.accuracy_score(y_test, y_pred)
    except:
        print("Let me reclassify AdaBoost again")

print("Accuracy of AdaBoost:", AdaBoost_accuracy)