机器学习之朴素贝叶斯分类算法

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机器学习之朴素贝叶斯分类算法www.shan-machinery.com一、数学知识相关

1.独立事件--前提

2.条件概率

3.全概率公式

4.贝叶斯公式

5.朴素贝叶斯公式

其中:

P(A)叫做A事件的先验概率,即一般情况下,认为A发生的概率。P(B|A)叫做似然度,是A假设条件成立的情况下发生B的概率。P(A|B)叫做后验概率,在B发生的情况下发生A的概率,也就是要求的概率。P(B)叫做标准化常量,即在一般情况下,认为B发生的概率。

朴素贝叶斯分类算法:朴素贝叶斯(Naive Bayes ,NB)算法是基于贝叶斯定理与特征条件独立假设的分类方法,该算法是有监督的学习算法,解决的是分类问题,是将一个未知样本分到几个预先已知类别的过程。朴素贝叶斯的思想就是根据某些个先验概率计算Y变量属于某个类别的后验概率,也就是根据先前事件的有关数据估计未来某个事件发生的概率。

二、理解朴素贝叶斯

假设现在有一堆邮件,正常邮件的比例是80%,垃圾邮件的比例是20%,这堆邮件中,5%的邮件中出现Viagra单词,如果有一封邮件,这封邮件中包含Viagra单词,求这封邮件是垃圾邮件的概率。

 

显然不能使用5%*20%=1%得到这封邮件是垃圾邮件的概率,因为垃圾邮件中有可能出现Viagra也有可能不会出现Viagra单词。那么根据贝叶斯公式可得包含Viagra单词的邮件是垃圾邮件的概率为:

 

通过同样的计算可以得到,含有Viagra单词但不是垃圾邮件的概率为0.2。那么可以认为这封邮件是垃圾邮件的概率比较大。这里的Viagra可以理解为邮件中的一个特征。那么当一封邮件有额外更多的特征时,贝叶斯如何扩展?

假设所有历史邮件中只出现了4个单词,也就是4个特征,根据历史邮件统计的单词频率表如下:

假设现在给定一封邮件中有Viagra和Unsubscribe(取消订阅)两个单词,求这封邮件是垃圾邮件的概率、不是垃圾邮件的概率?

利用贝叶斯公式,我们可以得到: 

三、拉普拉斯估计

根据以上例子,假设有一封邮件这4个单词都出现了,求这封邮件是垃圾邮件的概率:

由于的概率为0/20,会导致整个结果是垃圾邮件的概率为0,那么就否定了其他单词出现的权重。

拉普拉斯估计本质上是给频率表中的每个单词的计数加上一个较小的数,这样就保证每一类中每个特征发生的概率非零。通常,拉普拉斯估计中加上的数值为1,这样就保证了每一个特征至少在数据中出现一次。

四、Python 贝叶斯案例 import osimport sys# codecs 编码转换模块import codecsif __name__ == '__main__':# 读取文本构建语料库corpus = []labels = []corpus_test = []labels_test = []f = codecs.open("./sms_spam.txt", "rb")count = 0while True:# readline() 方法用于从文件读取整行,包括 "\n" 字符。line = f.readline().decode("utf-8")# 读取第一行,第一行数据是列头,不统计if count == 0:count = count + 1continueif line:count = count + 1line = line.split(",")label = line[0]sentence = line[1]corpus.append(sentence)if "ham" == label:labels.append(0)elif "spam" == label:labels.append(1)if count > 5550:corpus_test.append(sentence)if "ham" == label:labels_test.append(0)elif "spam" == label:labels_test.append(1)else:break# 文本特征提取:skl# 将文本数据转化成特征向量的过程# 比较常用的文本特征表示法为词袋法## 词袋法:# 不考虑词语出现的顺序,每个出现过的词汇单独作为一列特征# 这些不重复的特征词汇集合为词表# 每一个文本都可以在很长的词表上统计出一个很多列的特征向量# CountVectorizer是将文本向量转换成稀疏表示数值向量(字符频率向量)vectorizer 将文档词块化,只考虑词汇在文本中出现的频率# 词袋vectorizer = CountVectorizer()# 每行的词向量,fea_train是一个矩阵fea_train = vectorizer.fit_transform(corpus)print("vectorizer.get_feature_names is ", vectorizer.get_feature_names())print("fea_train is ", fea_train.toarray())# vocabulary=vectorizer.vocabulary_ 只计算上面vectorizer中单词的tf(term frequency 词频)vectorizer2 = CountVectorizer(vocabulary=vectorizer.vocabulary_)fea_test = vectorizer2.fit_transform(corpus_test)# print vectorizer2.get_feature_names()# print fea_test.toarray()# create the Multinomial Naive Bayesian Classifier# alpha = 1 拉普拉斯估计给每个单词个数加1clf = MultinomialNB(alpha=1)clf.fit(fea_train, labels)pred = clf.predict(fea_test);for p in pred:if p == 0:print("正常邮件")else:print("垃圾邮件")

测试结果:

C:\ProgramData\Anaconda3\python.exe F:/Python/001code/machinelearning/www/lxk/com/bayes.py*************************Naive Bayes*************************vectorizer.get_feature_names is['00', '008704050406', '0089', '0121', '01223585236', '01223585334', '0125698789', '02', '0207', '02073162414', '02085076972', '021', '03', '04', '0430', '05', '050703', '0578', '06', '07', '07008009200', '07046744435', '07090201529', '07123456789', '0721072', '07732584351', '07734396839', '07742676969', '07753741225', '0776xxxxxxx', '07786200117', '077xxx', '078', '07801543489', '07808', '07808247860', '07808726822', '07815296484', '07821230901', '078498', '07880867867', '0789xxxxxxx', '07946746291', '0796xxxxxx', '07973788240', '07xxxxxxxxx', '08', '0800', '08000407165', '08000776320', '08000839402', '08000930705', '08000938767', '08001950382', '08002888812', '08002986030', '08002986906', '08002988890', '08006344447', '0808', '08081263000', '08081560665', '0825', '083', '0844', '08448350055', '08448714184', '0845', '08452810071', '08452810073', '08452810075over18', '0870', '08700435505150p', '08700621170150p', '08701213186', '08701237397', '08701417012', '0870141701216', '087016248', '087018728737', '08702490080', '08702840625', '08704439680', '08706091795', '0870737910216yrs', '08707500020', '08707509020', '0870753331018', '08707808226', '08708034412', '08708800282', '08709501522', '0871212025016', '08712300220', '087123002209am', '08712317606', '08712400200', '08712400602450p', '08712402050', '08712402578', '08712402779', '08712402902', '08712402972', '08712404000', '08712405020', '08712405022', '08712460324', '0871277810710p', '0871277810810', '0871277810910p', '08714342399', '087147123779am', '08714712379', '08714712388', '08714712394', '08714712412', '08715203028', '08715203649', '08715203652', '08715203656', '08715203677', '08715203685', '08715203694', '08715205273', '08715500022', '08715705022', '08717111821', '08717168528', '08717205546', '0871750', '08717507382', '08717509990', '08717890890', '08717898035', '08718711108', '08718720201', '08718723815', '08718725756', '08718726270', '08718727868', '08718727870', '08718727870150ppm', '08718730666', '08718738001', '08718738002', '08718738034', '08719180219', '08719180248', '08719181259', '08719181503', '08719181513', '08719839835', '08719899217', '08719899229', '08719899230', '09', '09050000332', '09050000460', '09050001295', '09050001808', '09050002311', '09050003091', '09050090044', '09050280520', '09053750005', '09056242159', '09058091854', '09058091870', '09058094454', '09058094455', '09058094507', '09058094565', '09058094583', '09058094594', '09058094597', '09058094599', '09058095107', '09058095201', '09058097189', '09058097218', '09058098002', '09061104276', '09061104283', '09061209465', '09061213237', '09061221061', '09061221066', '09061701444', '09061701461', '09061701851', '09061701939', '09061702893', '09061743386', '09061743806', '09061743810', '09061743811', '09061744553', '09061749602', '09061790121', '09061790125', '09061790126', '09063440451', '09063442151', '09063458130', '0906346330', '09064012103', '09064012160', '09064015307', '09064017295', '09064018838', '09064019014', '09064019788', '09065171142', '09065174042', '09065394514', '09065394973', '09066350750', '09066358152', '09066358361', '09066362231', '09066364311', '09066368327', '09066368753', '09066380611', '09066382422', '09066612661', '09066649731from', '09066660100', '09071512432', '09071512433', '09077818151', '09090204448', '09090900040', '09094100151', '09094646631', '09094646899', '09095350301', '09096102316', '09099725823', '09099726395', '09099726429', '09099726481', '09099726553', '09111030116', '09111032124', '09701213186', '0a', '0quit', '10', '100', '1000', '1000call', '1000s', '100p', '100percent', '1013', '1030', '10am', '10k', '10p', '10ppm', '11', '1120', '1131', '114', '116', '1172', '118p', '11mths', '11pm', '12', '1205', '120p', '121', '1225', '123', '125', '1250', '125gift', '128', '12hours', '12hrs', '12mths', '13', '130', '14', '140', '1405', '140ppm', '145', '1450', '146tf150p', '14tcr', '15', '150', '150p', '150p16', '150pm', '150ppm', '150ppmsg', '151', '15541', '15pm', '16', '165', '169', '177', '18', '180', '18p', '18yrs', '195', '1apple', '1b6a5ecef91ff9', '1cup', '1da', '1lemon', '1million', '1pm', '1st', '1st4terms', '1stchoice', '1stone', '1thing', '1tulsi', '1win150ppmx3', '1winawk', '20', '200', '2000', '2003', '2004', '2005', '2006', '2007', '200p', '2025050', '20m12aq', '20p', '21', '21870000', '21st', '22', '220cm2', '2309', '23f', '23g', '24', '24hrs', '24m', '24th', '25', '250', '250k', '255', '25p', '26', '2667', '26th', '27', '28', '2814032', '28days', '28th', '28thfeb', '29', '2b', '2c', '2day', '2end', '2exit', '2find', '2getha', '2geva', '2go', '2kbsubject', '2lands', '2marrow', '2moro', '2morow', '2morro', '2morrow', '2morrowxxxx', '2mro', '2mrw', '2nd', '2nhite', '2nite', '2optout', '2p', '2price', '2rcv', '2stop', '2stoptx', '2stoptxt', '2u', '2u2', '2waxsto', '2wks', '2wt', '2years', '2yr', '2yrs', '30', '300', '3000', '300603', '300603t', '300p', '3030', '30ish', '30pm', '30s', '30th', '31', '3100', '310303', '31p', '32', '32000', '3230', '326', '33', '330', '350', '3510i', '3650', '36504', '3680', '3750', '37819', '38', '391784', '3d', '3days', '3g', '3hrs', '3lions', '3lp', '3miles', '3mins', '3mobile', '3pound', '3qxj9', '3rd', '3uz', '3wks', '3xx', '40', '400', '400mins', '4041', '40411', '40533', '40gb', '41685', '41782', '420', '42049', '4217', '42478', '42810', '430', '434', '44', '440', '44345', '447797706009', '447801259231', '448712404000', '449050000301', '449071512431', '45', '450', '450pw', '45239', '45pm', '4742', '47per', '48', '4882', '48922', '49', '49557', '4a', '4d', '4eva', '4fil', '4get', '4goten', '4info', '4jx', '4msgs', '4mths', '4t', '4th', '4the', '4txt', '4u', '4utxt', '4w', '4xx26', '4years', '50', '500', '5000', '505060', '50award', '50p', '50perweeksub', '50perwksub', '50pm', '50pmmorefrommobile2bremoved', '50ppm', '50rcvd', '515', '5226', '530', '54', '542', '545', '5ish', '5min', '5mls', '5p', '5pm', '5th', '5wq', '600', '6031', '6089', '60p', '61200', '61610', '62220cncl', '6230', '62468', '62735', '630', '63miles', '645', '65', '650', '6669', '674', '67441233', '68866', '69101', '69200', '69669', '69696', '69698', '69855', '69876', '69888', '69888nyt', '69911', '69969', '69988', '6hrs', '6ish', '6missed', '6months', '6pm', '6th', '6times', '6wu', '6zf', '700', '71', '7250', '7250i', '730', '74355', '75', '750', '7548', '75max', '762', '7634', '7684', '77', '7732584351', '786', '7am', '7cfca1a', '7ish', '7mp', '7oz', '7pm', '7th', '7zs', '80', '800', '8000930705', '80062', '8007', '80082', '80086', '80160', '80182', '80488', '80608', '8077', '80878', '81151', '81303', '81618', '820554ad0a1705572711', '82242', '82277', '82324', '83021', '83039', '83110', '83118', '83222', '83332', '83338', '83355', '83370', '83383', '83435', '83600', '84025', '84128', '84199', '84484', '85', '850', '85023', '85069', '85233', '8552', '85555', '86021', '861', '864233', '86688', '86888', '87021', '87066', '87070', '87077', '87121', '87131', '8714714', '87239', '87575', '8800', '88039', '88066', '88222', '88600', '88800', '8883', '88877', '88888', '89034', '89070', '89080', '89105', '89123', '89555', '89693', '8am', '8ball', '8lb', '8pm', '8th', '900', '9061100010', '910', '930', '945', '946', '95', '97n7qp', '98321561', '99', '9ae', '9am', '9ja', '9pm', '9t', '9th', '____', 'a21', 'a30', 'aah', 'aaniye', 'aaooooright', 'aathi', 'ab', 'abbey', 'abdomen', 'abeg', 'abel', 'aberdeen', 'abi', 'ability', 'abiola', 'abj', 'able', 'abnormally', 'about', 'aboutas', 'above', 'absence', 'absolutely', 'absolutly', 'abstract', 'abt', 'abta', 'abuse', 'ac', 'academic', 'acc', 'accent', 'accenture', 'accept', 'access', 'accessible', 'accidant', 'accident', 'accidentally', 'accommodation', 'accommodationvouchers', 'accomodations', 'accordin', 'accordingly', 'account', 'accounting', 'accounts', 'achan', 'ache', 'achieve', 'acl03530150pm', 'acnt', 'across', 'act', 'acted', 'actin', 'action', 'activate', 'active', 'activities', 'actor', 'actual', 'actually', 'ad', 'adam', 'add', 'added', 'addie', 'adding', 'address', 'adds', 'adewale', 'adi', 'admin', 'administrator', 'admirer', 'adore', 'adoring', 'adp', 'adress', 'adrian', 'ads', 'adsense', 'adult', 'adults', 'advance', 'adventuring', 'advice', 'aeronautics', 'aeroplane', 'afew', 'affair', 'affairs', 'affectionate', 'affidavit', 'afford', 'afghanistan', 'afraid', 'africa', 'african', 'aft', 'after', 'afternon', 'afternoon', 'afterwards', 'aftr', 'ag', 'again', 'against', 'agalla', 'age', 'age16', 'agency', 'agent', 'agents', 'ages', 'agidhane', 'aging', 'ago', 'agree', 'ah', 'aha', 'ahead', 'ahhh', 'ahhhh', 'aids', 'aight', 'ain', 'aint', 'air', 'air1', 'airport', 'airtel', 'aiya', 'aiyah', 'aiyar', 'aiyo', 'ajith', 'aka', 'akon', 'al', 'alaipayuthe', 'albi', 'album', 'alcohol', 'aldrine', 'alert', 'alertfrom', 'alex', 'alfie', 'algarve', 'algebra', 'algorithms', 'ali', 'alian', 'alibi', 'alive', 'all', 'allah', 'allday', 'alle', 'allo', 'allow', 'allowed', 'almost', 'alone', 'along', 'already', 'alright', 'alrite', 'also', 'alter', 'alternative', 'although', 'alto18', 'aluable', 'always', 'alwys', 'am', 'amanda', 'amazing', 'ambrith', 'american', 'ami', 'amk', 'amla', 'ammae', 'amongst', 'amount', 'amplikater', 'amrca', 'amrita', 'ams', 'amt', 'amused', 'amy', 'an', 'ana', 'anal', 'analysis', 'anand', 'and', 'anderson', 'andre', 'andres', 'andros', 'angels', 'angry', 'animal', 'animation', 'anjie', 'anjola', 'anna', 'annie', 'anniversary', 'annoncement', 'announced', 'announcement', 'annoying', 'anonymous', 'another', 'ans', 'ansr', 'answer', 'answered', 'answerin', 'answering', 'answers', 'answr', 'antelope', 'antha', 'anthony', 'anti', 'antibiotic', 'any', 'anybody', 'anyhow', 'anymore', 'anyone', 'anyones', 'anyplaces', 'anythiing', 'anythin', 'anything', 'anythingtomorrow', 'anytime', 'anyway', 'anyways', 'anywhere', 'aom', 'apart', 'apartment', 'apes', 'aphex', 'apnt', 'apo', 'apologetic', 'apologise', 'apologize', 'apology', 'app', 'apparently', 'appeal', 'appear', 'appendix', 'applausestore', 'applebees', 'apples', 'application', 'apply', 'applyed', 'applying', 'appointment', 'appointments', 'appreciate', 'appreciated', 'approaches', 'approaching', 'appropriate', 'approve', 'approved', 'approx', 'apps', 'appt', 'appy', 'april', 'aptitude', 'aquarius', 'ar', 'arcade', 'archive', 'ard', 'are', 'area', 'aren', 'arent', 'arestaurant', 'aretaking', 'areyouunique', 'argh', 'argue', 'arguing', 'argument', 'arguments', 'arithmetic', 'arm', 'armand', 'arms', 'arng', 'arngd', 'arnt', 'around', 'arr', 'arrange', 'arranging', 'arrested', 'arrive', 'arrived', 'arrow', 'arsenal', 'art', 'artists', 'arts', 'arty', 'arun', 'as', 'asa', 'asap', 'ashes', 'ashley', 'ashwini', 'asia', 'asian', 'asjesus', 'ask', 'askd', 'asked', 'askin', 'asking', 'asks', 'aslamalaikkum', 'asleep', 'asp', 'aspects', 'ass', 'assessment', 'assistance', 'associate', 'assume', 'asthere', 'asthma', 'astne', 'astrology', 'astronomer', 'asus', 'asusual', 'at', 'ate', 'athletic', 'athome', 'atlanta', 'atlast', 'atleast', 'atm', 'attach', 'attached', 'attack', 'attempt', 'atten', 'attend', 'attended', 'attending', 'attention', 'attitude', 'attraction', 'attractive', 'attracts', 'attributed', 'atural', 'auction', 'audiitions', 'audition', 'audrey', 'audrie', 'august', 'aunt', 'auntie', 'aunties', 'aunts', 'aunty', 'australia', 'authorise', 'auto', 'autocorrect', 'av', 'available', 'avalarr', 'avatar', 'avble', 'ave', 'avenge', 'avent', 'avenue', 'avin', 'avo', 'avoid', 'avoiding', 'avoids', 'await', 'awaiting', 'awake', 'award', 'awarded', 'away', 'awesome', 'awkward', 'aww', 'awww', 'ax', 'axel', 'ay', 'ayn', 'ayo', 'b4', 'b4280703', 'b4u', 'b4utele', 'ba', 'baaaaaaaabe', 'baaaaabe', 'babe', 'babes', 'babies', 'baby', 'babygoodbye', 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'update', 'update_now', 'upgrade', 'upgrading', 'upgrdcentre', 'upload', 'uploaded', 'upping', 'ups', 'upset', 'upstairs', 'upto', 'uptown', 'ur', 'urawinner', 'ure', 'urfeeling', 'urgent', 'urgently', 'urgh', 'urgnt', 'urgoin', 'urination', 'url', 'urmom', 'urn', 'urself', 'us', 'usb', 'usc', 'use', 'used', 'useful', 'useless', 'user', 'uses', 'usf', 'usher', 'using', 'usual', 'usually', 'uterus', 'utter', 'uup', 'uv', 'uworld', 'va', 'vaazhthukkal', 'vague', 'vaguely', 'vale', 'valentine', 'valentines', 'valid', 'valid12hrs', 'valuable', 'value', 'valued', 'values', 'valuing', 'varaya', 'various', 'varma', 'vary', 'vasai', 'vat', 'vatian', 'vava', 'vco', 'vday', 've', 'vegas', 'veggie', 'velachery', 'velly', 'venaam', 'verify', 'verifying', 'version', 'versus', 'very', 'vettam', 'vewy', 'via', 'vic', 'victoria', 'victors', 'vid', 'video', 'videochat', 'videophones', 'videos', 'videosound', 'videosounds', 'view', 'vijay', 'vijaykanth', 'vikky', 'vilikkam', 'vill', 'villa', 'village', 'violated', 'violence', 'violet', 'vip', 'vipclub4u', 'virgin', 'virgins', 'virtual', 'visa', 'visit', 'visiting', 'vitamin', 'vivek', 'vl', 'voda', 'vodafone', 'vodka', 'voice', 'voicemail', 'voila', 'volcanoes', 'vomit', 'vomiting', 'vote', 'voted', 'vouch4me', 'voucher', 'vouchers', 'vpod', 'vry', 'vth', 'vu', 'w1', 'w111wx', 'w1a', 'w1j6hl', 'w1jhl', 'w1t1jy', 'w4', 'w45wq', 'w8in', 'wa', 'waaaat', 'wad', 'wadebridge', 'wah', 'wahala', 'wahay', 'waheed', 'waheeda', 'waht', 'wait', 'waited', 'waitin', 'waiting', 'wake', 'waking', 'wales', 'waliking', 'walk', 'walkabout', 'walked', 'walkin', 'walking', 'walks', 'wall', 'wallpaper', 'walls', 'walmart', 'walsall', 'wamma', 'wan', 'wan2', 'wana', 'wanna', 'wannatell', 'want', 'want2come', 'wanted', 'wanting', 'wants', 'wap', 'waqt', 'warm', 'warned', 'warner', 'warning', 'warranty', 'was', 'washob', 'wasn', 'wasnt', 'waste', 'wasted', 'wasting', 'wat', 'watch', 'watched', 'watches', 'watchin', 'watching', 'watchng', 'water', 'watever', 'wating', 'watr', 'wats', 'watts', 'wave', 'wavering', 'waves', 'way', 'way2sms', 'waz', 'wc1n', 'wc1n3xx', 'we', 'weak', 'weakness', 'weaknesses', 'weapon', 'wear', 'wearing', 'weather', 'web', 'web2mobile', 'webadres', 'webeburnin', 'webpage', 'website', 'wed', 'weddin', 'wedding', 'weddingfriend', 'wednesday', 'wee', 'weed', 'week', 'weekdays', 'weekend', 'weekends', 'weekly', 'weeks', 'weigh', 'weighed', 'weight', 'weightloss', 'weird', 'weirdest', 'weirdo', 'weirdy', 'weiyi', 'welcome', 'welcomes', 'well', 'wellda', 'welp', 'wen', 'wendy', 'wenever', 'went', 'wenwecan', 'wer', 'were', 'werebored', 'weren', 'werethe', 'wesley', 'west', 'westlife', 'westonzoyland', 'westshore', 'wet', 'wetherspoons', 'wewa', 'whassup', 'what', 'whatever', 'whats', 'whatsup', 'wheellock', 'when', 'whenever', 'whenevr', 'whens', 'where', 'whereare', 'wherever', 'wherevr', 'wherre', 'whether', 'which', 'while', 'whilltake', 'whispers', 'white', 'whn', 'who', 'whole', 'whom', 'whore', 'whos', 'whose', 'whr', 'why', 'wi', 'wicked', 'wicket', 'wid', 'widelive', 'wif', 'wife', 'wifi', 'wihtuot', 'wil', 'wildlife', 'will', 'willing', 'willpower', 'win', 'win150ppmx3age16', 'wind', 'window', 'windows', 'winds', 'windy', 'wine', 'wined', 'wings', 'wining', 'winner', 'winning', 'wins', 'winterstone', 'wipro', 'wire3', 'wisdom', 'wise', 'wish', 'wisheds', 'wishes', 'wishin', 'wishing', 'wishlist', 'wiskey', 'wit', 'with', 'withdraw', 'wither', 'within', 'without', 'witot', 'witout', 'wiv', 'wizzle', 'wk', 'wkend', 'wkent', 'wkg', 'wkly', 'wknd', 'wld', 'wml', 'wn', 'wnevr', 'wnt', 'woah', 'wocay', 'woke', 'woken', 'woman', 'womdarfull', 'women', 'won', 'wondar', 'wondarfull', 'wonder', 'wonderful', 'wondering', 'wonders', 'wont', 'woo', 'woodland', 'woods', 'woohoo', 'woot', 'woould', 'word', 'words', 'work', 'workand', 'workin', 'working', 'workout', 'works', 'world', 'worlds', 'worms', 'worried', 'worries', 'worry', 'worrying', 'worse', 'worth', 'worthless', 'wot', 'wotu', 'would', 'woulda', 'wouldn', 'wow', 'wrc', 'wrench', 'wrenching', 'wright', 'write', 'writhing', 'wrk', 'wrkin', 'wrking', 'wrks', 'wrld', 'wrnog', 'wrong', 'wrongly', 'wrote', 'ws', 'wtc', 'wtf', 'wth', 'wud', 'wudn', 'wuld', 'wun', 'www', 'wylie', 'x2', 'xam', 'xavier', 'xchat', 'xclusive', 'xin', 'xmas', 'xoxo', 'xt', 'xuhui', 'xx', 'xxuk', 'xxx', 'xxxmobilemovieclub', 'xxxx', 'xxxxx', 'xxxxxx', 'xxxxxxx', 'xxxxxxxx', 'xy', 'y87', 'ya', 'yah', 'yahoo', 'yalrigu', 'yalru', 'yan', 'yar', 'yavnt', 'yaxx', 'yay', 'yck', 'yeah', 'year', 'years', 'yeesh', 'yeh', 'yelling', 'yellow', 'yelow', 'yep', 'yer', 'yes', 'yest', 'yesterday', 'yet', 'yetty', 'yetunde', 'yi', 'yifeng', 'yijue', 'ym', 'yo', 'yoga', 'yogasana', 'yor', 'yorge', 'you', 'youdoing', 'young', 'younger', 'youphone', 'your', 'youre', 'yourinclusive', 'yourjob', 'yours', 'yourself', 'youuuuu', 'youwanna', 'yoville', 'yowifes', 'yoyyooo', 'yr', 'yrs', 'ystrday', 'ything', 'yummmm', 'yummy', 'yun', 'yunny', 'yuo', 'yuou', 'yup', 'yupz', 'zaher', 'zed', 'zeros', 'zhong', 'zogtorius', 'zoom', 'zouk', 'zyada', 'œharry']fea_train is[[0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] ... [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0]]正常邮件正常邮件垃圾邮件正常邮件正常邮件正常邮件正常邮件正常邮件垃圾邮件正常邮件 四、Scala 贝叶斯案例 object Naive_bayes {def main(args: Array[String]) {//1 构建Spark对象val conf = new SparkConf().setAppName("Naive_bayes").setMaster("local")val sc = new SparkContext(conf)//读取样本数据1val data = sc.textFile("sample_naive_bayes_data.txt")val parsedData = data.map { line =>val parts = line.split(',')LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' 、').map(_.toDouble)))}//样本数据划分训练样本与测试样本val splits = parsedData.randomSplit(Array(0.9, 0.1), seed = 11L)val training = splits(0)val test = splits(1)//新建贝叶斯分类模型模型,并训练 ,lambda 拉普拉斯估计val model = NaiveBayes.train(training, lambda = 1.0)//对测试样本进行测试val predictionAndLabel = test.map(p => (model.predict(p.features), p.label))val print_predict = predictionAndLabel.take(10020)println("prediction" + "\t" + "label")for (ix._1 == x._2).count() / test.count()println(accuracy)//保存模型val ModelPath = "naive_bayes_model"model.save(sc, ModelPath)val sameModel = NaiveBayesModel.load(sc, ModelPath)}}

 

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