machine learning bayes theorem

\%s.txt'I'R'). Read () forIinchRange (1, 20)] Transpamlist= [Open (r'C:\Users\Administrator\Desktop\machinelearninginaction\Ch04\email\spam\%s.txt'I'R'). Read () forIinchRange (1, 20)] forLineinchtranhamlist:temp. Set_tran_data (line, True) forLineinchtranspamlist:temp. Set_tran_data (line, False) testlist= [Open (r'C:\Users\Administrator\Desktop\machinelearninginaction\Ch04\email\ham\%s.txt'I'R'). Read () forIinchRange (21, 26)] forLineinchtestlist:PrintTemp.classifiy (line)The

equivalent to the maximum likelihood estimation method.) ）Let me deduce the whole process of the maximal posteriori probability estimation method:According to the Bayes theorem: We take the characteristics of the dataset (features) and the tag (label) in it to get:.Because it is a constant (constant), the formula can be rewritten as:. (∝ expressed in direct proportion)Naive

Learning notes of machine learning practice: Classification Method Based on Naive Bayes, Probability is the basis of many machine learning algorithms. A small part of probability knowledge is used in the decision tree generation

, or even smaller after a very small concatenation, or even roundOff 0. This will affect the judgment, so they will be transferred to the logarithm space for calculation, the logarithm is often used in machine learning, to avoid ambiguity caused by the numerical operation while keeping monotonous, in addition, the logarithm can be used to convert multiplication to addition to accelerate the operation. There

equal to 1.5789 (greater than 1 is not related, since this is the value of the density function and is used only to reflect the relative probability of each value).With this data, the gender classification can be calculated. P (Height =6| male) x p (weight =130| male) x P (foot Palm =8| male) x p (male)= 6.1984 x e-9 P (Height =6| female) x p (weight =130| female) x P (foot Palm =8| female) x P (female)= 5.3778 x e-4 It can be seen that the probability of a woman is nearly 10,

parameter, which defaults to 1.0 and we set it to 0.01.nbc_6 = Pipeline([ (‘vect‘, TfidfVectorizer( stop_words=stop_words, token_pattern=ur"\b[a-z0-9_\-\.]+[a-z][a-z0-9_\-\.]+\b", )), (‘clf‘, MultinomialNB(alpha=0.015) [0.91073796 0.92532037 0.91604065 0.91294741 0.91202476]Mean score:0.915 (+/-0.003) This score has been optimized for the better.Evaluating classifier PerformanceWe have obtained better classifier parameters by cross-v

1, the definition of the modelNaive Bayes is a splitting method based on Bayesian theorem and independent hypothesis of characteristic condition. First, let's understand the Bayesian theorem and the model to be established. For a given data set　　Suppose the output category yi∈{c1, C2, ...., ck}, Naive Bayes learns the

Naive Bayesian Classification (NBC) is the most basic classification method in machine learning, and it is the basis of the comparison of classification performance of many other classification algorithms, and the other algorithms are based on NBC in evaluating performance. At the same time, for all machine learning me

Naive BayesianThis course outline:1. naive Bayesian- naive Bayesian event model2. Neural network (brief)3. Support Vector Machine (SVM) matting – Maximum interval classifierReview:1. Naive BayesA generation learning algorithm that models P (x|y).Example: Junk e-mail classificationWith the mail input stream as input, the output Y is {0,1},1 as spam, and 0 is not junk e-mail.Represents the message text as an

(that is, Xi in {1,..., | v|} Value in | V| is the vocabulary of the lexicon), n-word messages will be represented by a vector of length n, and the length of the vectors for different articles will probably not be the same.In the multiple event model, we assume that this is the case with the message: first determine whether this is a spam message through P (Y), and then independently determine each word by multiple distributions P (x|y). The probability of the final generation of the entire mes

| all) = P (all | no) P (NO)/P (all) = P (Sunny | no) P (cool | No) P (high | no) P (true | no) P (NO)/P (all) = 3/5*1/5*4/5*3/5*5/14/P (all) = 0.021/P (all) Therefore, the probability of no is high. Therefore, sunny, cool, high, and true should not play the game. Note that the table has a data value of 0, which means that when Outlook is overcast, if you do not play the ball or the probability is 0, you must play the ball as long as it is overcast, this violates the basic assumption of Naive

Probability-based classification method: Naive BayesianBayesian decision theoryNaive Bayes is part of the Bayesian decision theory, so let's take a quick and easy look at Bayesian decision theory before we talk about naive Bayes.The core idea of Bayesian decision-making theory : Choose the decision with the highest probability. For example, we graduate to choose the direction of employment, the probability of choosing C + + is 0.3, the probability of

understand. Search, about half an hour can be thoroughly understood.Support Vector machines. SearchHttp://www.cnblogs.com/jerrylead/archive/2011/03/13/1982639.htmlAnd I like a lot of applications of wild programmers have resonance, even if 100 saw what I share a person has been harvested value.When I was young, I wanted to make weapons so that everyone could have the power to change the world, grow up for so many years, and know that things are going to die fast, sharing weapons is power, and s

; - for(Auto d:data) { Wu for(inti =0; I i) { -C_p[make_pair (D[i], label)] + = (1.0/(Prior *data.size ())); About } $ } - } - } - A intNaivebayes::p redict (Constvectorint> Item) { + intresult; the DoubleMax_prob =0.0; - for(Auto p:p_p) { $ intLabel =P.first; the DoublePrior =P.second; the DoubleProb =Prior; the for(inti =0; I 1; ++i) { theProb *=C_p[make_pair (Item[i], label)]; - } in the

, or K nearest neighbor (Knn,k-nearestneighbor) classification algorithm, is one of the simplest methods in data mining classification technology. The so-called K nearest neighbor is the meaning of K's closest neighbour, saying that each sample can be represented by its nearest K-neighbor.The core idea of the KNN algorithm is that if the majority of the k nearest samples in a feature space belong to a category, the sample also falls into this category and has the characteristics of the sample on

NLF said that without considering specific problems, none of the algorithms were better than the other, and not even guesswork. Without a specific application, the universally applicable "optimal classifier" learning algorithm must make a "hypothesis" related to the problem domain, and the classifier must adapt to the problem field. However, the premise of the NFL theorem is that all problems appear equal

Students in the field of machine learning know that there is a universal theorem in machine learning: There is no free lunch (no lunch). The simple and understandable explanation for it is this: 1, an algorithm (algorithm a) on a specific data set than the performance of a

What is history, history is us, not you, not him, not her, is all people.—————————— PrefaceThis article is a summary of Bo Master's reading about Bayes and its related knowledge.first, the mathematical beauty of the article: the ordinary and magical Bayesian methodtheory and practice of machine learning (III.) naive Bayesianthree, from the Bayesian approach to th

Bayesian theorem is a kind of classification method of statisticsThe simplest Bayesian classification method is called naive Bayesian classification methodAn important condition of naive Bayesian method is that the effect of one attribute value on classification is independent of other attribute values, also known as class conditional independenceP (h| X) =p (x| h) P (h)/P (X) which has been known to seek the former. That is, a posteriori = likelihood

Reference: cs229 Handout Machine Learning (a): Generating learning algorithms generative Learning Algorithms : http://www.cnblogs.com/zjgtan/ archive/2013/06/08/3127490.html First, a simple comparison of the discriminant Learning algorithm (disc