来自两个机器学习模型的合并结果

Question

我有两个机器学习模型，每个目标我一个人运行，现在我希望将两者连接起来以获得一个结果...

其中一个模型包含tf-idf和target的文本，另一个包含目标6个属性的文本，这意味着我的所有数据都包含6个属性，所以我希望成为一个模型

第一个具有两个功能

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
DTClass = DecisionTreeClassifier(criterion="gini", splitter="best", 
 random_state=77)
X_train, X_test, y_train, y_test = train_test_split(bow, 
 df1["attacktype1_txt"], test_size = 1/5, random_state = 50)
DTClass.fit(X_train,y_train)
prediction = DTClass.predict(X_test)
from sklearn.metrics import accuracy_score
print("accuracy score:")
print(accuracy_score(y_test, prediction))

和第二个

array = df.values
X = array[:,1:7]
Y = array[:,7]
 validation_size = 0.20
seed = 4
X_train, X_validation, Y_train, Y_validation = 
 model_selection.train_test_split(X, Y, test_size=validation_size, 
  random_state=seed)
    seed = 4
      scoring = 'accuracy'
      models.append(('CART', DecisionTreeClassifier()))
       results = []
     names = []
     for name, model in models:
    kfold = model_selection.KFold(n_splits=10, random_state=seed)
    cv_results = model_selection.cross_val_score(model, X_train, Y_train, 
    cv=kfold, scoring=scoring)
     results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)

Answer 1

您的问题似乎与合并模型无关，而与合并数据有关。除非您有理由假设通过包含数据会降低模型性能，否则应避免因拆分模型而丢失信息。