我需要能够处理数据集，应用我的分类算法(我选择了3个朴素的bayes版本)，打印精度得分到终端，并执行5到10倍交叉验证，找出有多少电子邮件是垃圾邮件。

正如你所看到的，我已经完成了一些任务，但是没有进行交叉验证，也没有发现有多少电子邮件是垃圾邮件。import numpy as np

import pandas as pd

import sklearn

from sklearn.naive_bayes import BernoulliNB

from sklearn.naive_bayes import GaussianNB

from sklearn.naive_bayes import MultinomialNB

from sklearn.model_selection import train_test_split

from sklearn import metrics

from sklearn.metrics import accuracy_score

# Read data

dataset = pd.read_csv('dataset.csv').values

# What shuffle does? How it helps?

np.random.shuffle(dataset)

X = dataset[ : , :48 ]

Y = dataset[ : , -1 ]

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = .33, random_state = 17)

# Bernoulli Naive Bayes

BernNB = BernoulliNB(binarize = True)

BernNB.fit(X_train, Y_train)

y_expect = Y_test

y_pred = BernNB.predict(X_test)

print ("Bernoulli Accuracy Score: ")

print (accuracy_score(y_expect, y_pred))

# Multinomial Naive Bayes

MultiNB = MultinomialNB()

MultiNB.fit(X_train, Y_train)

y_pred = MultiNB.predict(X_test)

print ("Multinomial Accuracy Score: ")

print (accuracy_score(y_expect, y_pred))

# Gaussian Naive Bayes

GausNB = GaussianNB()

GausNB.fit(X_train, Y_train)

y_pred = GausNB.predict(X_test)

print ("Gaussian Accuracy Score: ")

print (accuracy_score(y_expect, y_pred))

# Bernoulli ALTERED Naive Bayes

BernNB = BernoulliNB(binarize = 0.1)

BernNB.fit(X_train, Y_train)

y_expect = Y_test

y_pred = BernNB.predict(X_test)

print ("Bernoulli 'Altered' Accuracy Score: ")

print (accuracy_score(y_expect, y_pred))

我已经研究了交叉验证，认为我现在可以应用这个，但它发现有多少电子邮件是垃圾邮件，我不明白？？？我有不同的navie bayes版本的准确性，但我如何才能真正找到垃圾邮件的数量？最后一列是1或0，它定义了它是否是垃圾邮件？所以我不知道该怎么做