naive bayes algorithm

1. Introduction to naive Bayesian algorithmOne to classify x= (A,b,c ... ), judging x belongs to Y1,y2,y3 ... Which class of the category.Bayesian formula:The algorithm is defined as follows:(1), set X={A1, A2, A3, ...} For one to classify, while A1, A2, A3 ... Characteristics of X, respectively(2), there are categories set c={y1, y2, Y3, ...}(3), calculated P (y1|x), P (y2|x), P (y3|x), ....(4), if P (Y (k

1. PrefaceTagging a large number of text data that needs to be categorized is a tedious, time-consuming task, while the real world, such as the presence of large amounts of unlabeled data on the Internet, is easy and inexpensive to access. In the following sections, we introduce the use of semi-supervised learning and EM algorithms to fully combine a large number of unlabeled samples in order to obtain a higher accuracy of text classification. This article uses the polynomial

document vector space, a fixed class collection C={c1,c2,..., CJ}, and a category called a label. Obviously, the document vector space is a high dimensional space. We have a bunch of tagged documents set For this one-sentence document, we classify it in the US, which is labeled "the". We expect to use some kind of training algorithm to train a function γ to map documents to a certain category: Γ:x→c This type of learning is called supervised learni

Classification algorithm pay attention to the beginning algorithm to find the probability characteristic parameter of the prior probability condition the continuous mean of the discrete characteristic parameter and the standard deviation to find the probability of conditional probabilities based on mean and standard deviation Naive Bayesian

Bayesian classification is an algorithm using probability and statistic knowledge to classify, and its classification principle is Bayesian theorem. The Bayesian theorem has the following formula:650) this.width=650; "Src=" Http://s2.51cto.com/wyfs02/M02/8D/50/wKiom1iXH7qzQ3X2AAAI9To-mac657.png-wh_500x0-wm_3 -wmp_4-s_3022789441.png "Title=" Bayes theorem. png "alt=" wkiom1ixh7qzq3x2aaai9to-mac657.png-wh_50

= Np.random.randint (2, Size= (6, 100)) >>> Y = Np.array ([1, 2, Span class= "Hljs-number" >3, 4, 4, 5] ) >>> from Sklearn.naive_bayes import bernoullinb>>> CLF = BernoulliNB () >>> clf.fit (X, Y) bernoullinb (Alpha=1.0, Binarize=0.0, Class_prior=none, Fit_prior=True) >>> print (clf.predict (x[2])) [3 ] The BERNOULLINB () class also has a Partial_fit () function.The application of polynomial model and Bernoulli model in text classification A good explanation is given in the text classific

Preface In the previous time has studied the NB naive Bayesian algorithm, and just a preliminary study of Bayesian network of some basic concepts and commonly used computational methods. So there is the first knowledge of Bayesian network article, because I have been studying learning naive Bayesian algorithm We have

How is naive Bayesian algorithm understood?Naive Bayesian algorithm is an algorithm of generative formulaOur goal is to classify the current instance of X as that category, but the resulting formula is the P (ck/x)In practical problems we usually know that P (Ck) is called a

algorithms--the observations are independent and unrelated to each other. Because if you are independent, you can split the probability formula into this: P (1110a| C1) =p (111| C1) *p (0| C1) *p (a| C1), it will be easier to calculate and less likely to be equal to 0. p (c1|1110a) = P (1110a| C1) *p (C1) = P (111| C1) *p (0| C1) *p (a| C1) *p (C1) =1/6*4/5*4/5*5/7=0.076p (c2|1110a) = P (1110a| C2) *p (C2) = P (111| C2) *p (0| C2) *p (a| C2) *p (C2) =1/3*1/2*1/3*2/7=0.016C1 analogy C2 class pro

Advantages and disadvantages of algorithms Pros: Still effective with less data, can handle multiple categories of problems Cons: Sensitive to the way the input data is prepared Applicable data type: Nominal type data Algorithm idea: Naive Bayesian For example, we want to determine whether an e-mail message is spam, then we know the distribution of the word in this message, then we also need to know: spam

1. Background When I was outside the company internship, a great God told me that learning computer is to a Bayesian formula applied to apply. Well, it's finally used. Naive Bayesian classifier is said to be a lot of anti-Vice software used in the algorithm, Bayesian formula is also relatively simple, the university to do probability problems often used. The core idea is to find out the most likely effect

In this paper, the Python implementation method of naive Bayesian algorithm is described. Share to everyone for your reference. The implementation method is as follows: Advantages and disadvantages of naive Bayesian algorithm Pros: Still effective with less data, can handle multiple categories of problems Cons: Sensit

PrefaceRecently on the data mining learning process, learn to naive Bayesian operation Roc Curve. It is also the experimental subject of this section, the calculation principle of ROC curve and if statistic TP, FP, TN, FN, TPR, FPR, ROC area and so on. The ROC area is often used to assess the accuracy of the model, generally think the closer to 0.5, the lower the accuracy of the model, the best state is close to 1, the correct model area is 1. The fol

Algorithm Description:Input: Training data $t={(X_{1},y_{1}), (X_{2},y_{2}),..., (X_{n},y_{n})}$, where $x_{i}= (x_{i}^{(1)},x_{i}^{(2)},..., x_{i}^{(N)}) $ , $x _{i}^{(j)}$ is the J-feature of the sample I, $x _{i}^{(j)}\in \{a_{j1},a_{j2},..., A_{js} \}$, $a _{jl}$ represents the possible L-values of the J-Features, j=1,2,..., n,l= ,..., Sj, $y _{i} \in \{c_{1},c_{2},..., c_{k} \}$; instance x;Output: Classification of Instance X(1) Calculate prior

Algorithm KNNThe main idea of the algorithm:1 Select the nearest sample point for K and to-classify points2 look at the classification of the sample points in 1, voting determines the class to which the classification points belongBayesian classifierBackground: Naive Bayesian text classifier principleBayes is everywhereAoccdrnigto a rscheearchat cmabrigdeuinervt

belongs to the category is:", self.classlabel[location]filename= "D:\MyDocuments-HnH\DataMining\DataSets\Car\Car_Data.txT "Mycar=carbayesian () mycar.loaddatas (filename, ', ') attributes=[' buying ', ' maint ', ' doors ' , ' persons ', ' lug_boot ', ' Safety ']mycar.getattrilist (attributes) Mycar.getdesclass (7-1) mycar.tranning () sample =[' Vhigh ', ' Vhigh ', ' 2 ', ' 2 ', ' small ', ' low ']This article is from "Lu Yao" blog, please be sure to keep this source http://cwxfly.blog.51cto.co

Advantages and disadvantages of algorithms Pros: Still effective with less data, can handle multiple categories of problems Cons: Sensitive to the way the input data is prepared Applicable data type: Nominal type data Algorithm idea: Naive Bayesian For example, we want to determine whether an e-mail message is spam, then we know the distribution of the word in this message, then we also need to know: spam

The/** * naive string algorithm looks for substrings through two loops, * as if a "template" containing a pattern slides along an identifying text. * The idea of the algorithm is: from the main string s of the first POS word Fu Qi and pattern string comparison, * when the match is unsuccessful, from the main string s of the first pos+1 character back to the pat

): p1=sum (Vec2classify*p1vec) + np.log (PCLASS1) p0=sum (Vec2classify*p0vec) + Np.log (1.0NBSP;-NBSP;PCLASS1) ifp1>p0: return1else: return0# test Method DEFNBSP;TESTINGNB ():listoposts,listclasses= Loaddataset () myvocablist=createvocablist (listoposts) trainMat=[]forpostinDocinlistOPosts: trainmat.appenD (Setofwords2vec (Myvocablist,postindoc)) p0v,p1v,pab=trainnb0 ( Np.array (Trainmat), Np.array (listclasses)) testentry=[' love ', ' my ', ' dalmation ']thisdoc=np.array (Setofwords2vec (myVo

= text_parse (open (' email/ham/%d.txt '% i). Read ())Doc_list.append (word_list)Class_list.append (0)Vocab_list = Create_vocab_list (doc_list)Training_set = Range (50)Test_set = []# Choose 10 randomly from 50 messages as a test set, and reject the 10 messages in the training set accordingly.For I in Xrange (10):rand_index = Int (random.uniform (0, Len (training_set)))Test_set.append (Training_set[rand_index])Del (Training_set[rand_index])Train_mat = []Train_classes = []For Doc_index in Trainin