# Train a multinomial naive Bayes classifier classifier = MultinomialNB(alpha=0)# notice I use alpha=0 here because I control the dataset and know there are no 'empty' feature classifier.fit(X_train_counts, y_train) forclass_, count_, feature_count_inzip(classifier.classes_, classifier.clas...
# Train a multinomial naive Bayes classifier classifier = MultinomialNB(alpha=0) # notice I use alpha=0 here because I control the dataset and know there are no "empty" feature classifier.fit(X_train_counts, y_train) forclass_, count_, feature_count_inzip(classifier.classes_, classifier.cl...
详情可以看视频链接,讲的非常好。 1#coding=utf-82from__future__importdivision3fromnumpyimportarray45defnaive_bs(failed_number, drunk_number, shopping_number, study_number, expected_drunk, expected_shopping, expected_study):67ex_failed =08ex_not_failed =0910foriinrange(0, len(failed_number)):1...
X_train_counts=vectorizer.fit_transform(X_train)# Train a multinomial naive Bayes classifier classifier=MultinomialNB(alpha=0)# noticeIuse alpha=0here becauseIcontrol the dataset and know there are no"empty"feature classifier.fit(X_train_counts,y_train)forclass_,count_,feature_count_inzip(classi...
""" compute the accuracy of your Naive Bayes classifier """ ### import the sklearn module for GaussianNB from sklearn.naive_bayes import GaussianNB ### create classifier clf = GaussianNB()#TODO ### fit the classifier on the training features and labels ...
else:return0deftest(self):self.loadDataSet('testNB.txt')self.train()print(self.classify([1,2]))if__name__ =='__main__':NB = NaiveBayesClassifier()NB.test() Matlab# Matlab的标准工具箱提供了对朴素贝叶斯分类器的支持: trainData = [0 1; -1 0; 2 2; 3 3; -2 -1;-4.5 -4; ...
importjiebafrom nltk.classifyimportNaiveBayesClassifier # 需要提前把李白的诗收集一下,放在libai.txt文本中。 text1 = open(r"libai.txt","rb").readlist1 = jieba.cut(text1)result1 =" ".join(list1)# 需要提前把杜甫的诗收集一下,放在dufu.txt文本中。text2 = open(r"dufu.txt","rb").readli...
我已经训练了两个模型,即朴素贝叶斯分类器(Naive Bayes classifier)和支持向量机(SVM)。对于文档分类问题,朴素贝叶斯分类器是一种常规的并且非常流行的方法。它是一个基于贝叶斯定理的监督概率分类器,其假设每对特征之间是独立的。支持向量机是监督式的二元分类器,在你拥有更多的特征时它非常有效。支持向量机(SVM)的...
我已经训练了两个模型,即朴素贝叶斯分类器(Naive Bayes classifier)和支持向量机(SVM)。对于文档分类问题,朴素贝叶斯分类器是一种常规的并且非常流行的方法。它是一个基于贝叶斯定理的监督概率分类器,其假设每对特征之间是独立的。支持向量机是监督式的二元分类器,在你拥有更多的特征时它非常有效。支持向量机(SVM)的...
# Paste code for non-word removal here(code snippet is given below) return 词典创建好之后,我们只要在上面函数的基础上再加几行代码,就可以移除之前提到的那些非文字类符号了。这里我还顺手删掉了一些与垃圾邮件的判定无关的单字符,具体参见如下的代码,注意这些代码要附在 def make_Dictionary(train_dir) 函...