我会有一个关于如何在分类问题中添加更多分类字段的问题。我的数据集最初有 4 个字段：

Date             Text                            Short_Mex                        Username        Label
01/01/2020       I am waiting for the TRAIN      A train is coming                Ludo       1
01/01/2020       you need to keep distance       Social Distance is mandatory     wgriws    0
...
02/01/2020       trump declared war against CHINESE technology      China’s technology is out of the games      Fwu32      1

...

我将此数据集加入到一个带有标签的新数据集，其值为 1 或 0。这将需要分类。

但是，我还从原始数据集中提取了其他字段，例如字符数、大写单词、最常用的术语等。其中一些字段可能对分类有用，因为我可以根据大写而不是小写的单词分配更多的“权重”。

所以我需要使用带有这些字段的新数据集：

  Date             Text                            Short_Mex                        Username    Upper    Label
    01/01/2020       I am waiting for the TRAIN      A train is coming                Ludo    [TRAIN]       1
    01/01/2020       you need to keep distance       Social Distance is mandatory     wgriws       []      0
    ...
    02/01/2020       trump declared war against CHINESE technology      China’s technology is out of the games      Fwu32    [CHINESE]       1
...

我想问您如何将此信息（大写）添加为我的分类器的新信息。我目前正在做的事情如下：

#Train-test split
x_train,x_test,y_train,y_test = train_test_split(df['Text'], news.target, test_size=0.2, random_state=1)




    #Logistic regression classification
    pipe1 = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('model', LogisticRegression())])
    
    model_lr = pipe1.fit(x_train, y_train)

lr_pred = model_lr.predict(x_test)

print("Accuracy of Logistic Regression Classifier: {}%".format(round(accuracy_score(y_test, lr_pred)*100,2)))
print("\nConfusion Matrix of Logistic Regression Classifier:\n")
print(confusion_matrix(y_test, lr_pred))
print("\nCLassification Report of Logistic Regression Classifier:\n")
print(classification_report(y_test, lr_pred))