我正在做一个小练习，以确定电子邮件是否为垃圾邮件。我的数据集如下：

                       Email                                                   Spam
0   Hi, I am Andrew and I want too buy VIAGRA                                   1
1   Dear subscriber, your account will be closed                                1
2   Please click below to verify and access email restore                       1
3   Hi Anne, I miss you so much! Can’t wait to see you                          0
4   Dear Professor Johnson, I was unable to attend class today                  0
5   I am pleased to inform you that you have won our grand prize.               1
6   I can’t help you with that cuz it’s too hard.                               0
7   I’m sorry to tell you but im sick and will not be able to come to class.    0
8   Can I see an example before all are shipped or will that cost extra?        0
9   I appreciate your assistance and look forward to hearing back from you.     0

其中 1 表示垃圾邮件，0 不是垃圾邮件。我尝试过的是以下内容：

#Tokenization 

def fun(t):

# Removing Punctuations
remove_punc = [c for c in text if c not in string.punctuation]
remove_punc = ''.join(remove_punc)

# Removing StopWords
cleaned = [w for w in remove_punc.split() if w.lower() not in stopwords.words('english')]

return cleaned

所以我应用了函数：df['Email'].apply(fun)。然后我将文本转换为矩阵，如下所示：

from sklearn.feature_extraction.text import CountVectorizer
mex = CountVectorizer(analyzer= fun).fit_transform(df['Email'])

并将数据集拆分为训练和测试：

X_train, X_test, y_train, y_test = train_test_split(mex, df['Email'], test_size = 0.25, random_state = 0)

我应用了一个分类器（我会应用逻辑回归来确定电子邮件是否为垃圾邮件，但我目前只使用朴素贝叶斯：

from sklearn.naive_bayes import MultinomialNB

classifier = MultinomialNB()
classifier.fit(X_train, y_train)

最后，我首先将分类器应用于训练集，然后应用于测试集：

from sklearn.metrics import classification_report,confusion_matrix, accuracy_score
from sklearn.metrics import classification_report,confusion_matrix, accuracy_score

pred = classifier.predict(X_test)

print(classification_report(y_test ,pred ))
print('Confusion Matrix: \n', confusion_matrix(y_test,pred))
print()
print('Accuracy: ', accuracy_score(y_test,pred))

该代码有效，但我想知道如何通过新电子邮件示例直观地查看它是否具有标签 1 或 0。例如：如果我有一封新电子邮件'Hi, my name is Christopher and I like VIAGRA'，我如何确定标签/类别？

我觉得我遗漏了一些东西，或者我可能采用了错误的方式来证明这一点。

我的问题如下：

鉴于这封新电子邮件：Hi, my name is Christopher and I like VIAGRA，我如何查看这是否是垃圾邮件？我曾考虑过分类，但可能我的方法是错误的。我想要类似的东西：

Email                                        Spam 
... 
Hi, my name is Christopher and I like VIAGRA 1

因为这与电子邮件非常相似 'Hi, I am Andrew and I want too buy VIAGRA'（如果包含在训练集中或在测试集中正确预测）。

也许我想做的只需要tf-idf算法或不同的方法。任何建议将被认真考虑。