Bagging model machine learning : 
I have the following line of code:

# Setting the values ​​for the number of folds

    num_folds = 10
    seed = 7

# Separating data into folds

    kfold = KFold(num_folds, True, random_state = seed)

# Create the unit model (classificador fraco)

    cart = DecisionTreeClassifier()

# Setting the number of trees

    num_trees = 100

# Creating the bagging model

    model = BaggingClassifier(base_estimator = cart, n_estimators = num_trees, random_state = seed)

# Cross Validation

    resultado = cross_val_score(model, X, Y, cv = kfold)

# Result print

    print("Acurácia: %.3f" % (resultado.mean() * 100))

This is a ready-made code that I got from the internet, which is obviously predefined for testing my cross-validated TRAINING data and knowing the accuracy of the bagging algorithm.

I would like to know if I can apply it to my TEST data (data without output 'Y')


The code is a bit confusing and I can't model it.

I'm looking for something like:

# Training the model

    model.fit(X, Y)

# Making predict

    Y_pred = model.predict(X_test)

I want to use the trained bagging model on top of the training data in the test data and make predictions but I don't know how to modify the code