11.22作业

1.使用朴素贝叶斯模型对iris数据集进行花分类

尝试使用3种不同类型的朴素贝叶斯：

高斯分布型

from sklearn import datasets
iris = datasets.load_iris()

from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
pred = gnb.fit(iris.data, iris.target)
y_pred = pred.predict(iris.data)

print(iris.data.shape[0],(iris.target != y_pred).sum())

多项式型

from sklearn import datasets
iris = datasets.load_iris()

from sklearn.naive_bayes import MultinomialNB
gnb = MultinomialNB()
pred = gnb.fit(iris.data, iris.target)
y_pred = pred.predict(iris.data)

print(iris.data.shape[0],(iris.target != y_pred).sum())

伯努利型

from sklearn import datasets
iris = datasets.load_iris()

from sklearn.naive_bayes import BernoulliNB
gnb = BernoulliNB()
pred = gnb.fit(iris.data, iris.target)
y_pred = pred.predict(iris.data)

print(iris.data.shape[0],(iris.target != y_pred).sum())

2.使用sklearn.model_selection.cross_val_score()，对模型进行验证。

from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score
gnb = GaussianNB()
acores = cross_val_score(gnb, iris.data, iris.target, cv=10)
print("Accuracy:%.3f"%acores.mean())

from sklearn.naive_bayes import BernoulliNB
from sklearn.model_selection import cross_val_score
gnb = BernoulliNB()
acores = cross_val_score(gnb, iris.data, iris.target, cv=10)
print("Accuracy:%.3f"%acores.mean())

from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import cross_val_score
gnb = MultinomialNB()
acores = cross_val_score(gnb, iris.data, iris.target, cv=10)
print("Accuracy:%.3f"%acores.mean())

 

3. 垃圾邮件分类

数据准备：

• 用csv读取邮件数据，分解出邮件类别及邮件内容。
• 对邮件内容进行预处理：去掉长度小于3的词，去掉没有语义的词等

尝试使用nltk库：

pip install nltk

import nltk

nltk.download

不成功：就使用词频统计的处理方法

 

训练集和测试集数据划分

• from sklearn.model_selection import train_test_split

 

import csv
file_path = r"C:/Users/Administrator/Desktop/SMSSpamCollectionjsn.txt"
sms = open(file_path,'r',encoding = 'utf-8')
sms_data = []
sms_label = []
csv_reader = csv.reader(sms,delimiter='\t')
for line in csv_reader:
    sms_label.append(line[0])
    sms_data.append(line[1])
sms.close()
sms_data

sms_label

 

 

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(sms_data,sms_label,test_size=0.3,random_state=0,stratify=sms_label)

 

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(sms_data,sms_label,test_size = 0.3,random_state=0,stratify=sms_label)

x_train
x_test