naive bayes algorithm pseudocode

)) - PrintTestentry,'classified as:', CLASSIFYNB (thisdoc,p0v,p1v,pab) +Testentry = ['Stupid','Garbage'] -Thisdoc =Array (Setofword2vec (Myvocablist, testentry)) + PrintTestentry,'classified as:', CLASSIFYNB (THISDOC,P0V,P1V,PAB)Try the results of our calculations:Like the results we expected, the document words were correctly categorized.Summary:Naive Bayesian algorithm is more complex than decision tree and KNN

PART0 discriminant Learning AlgorithmIntroduced: Two-dollar classification problemModeling: discriminant Learning Algorithm (discriminative learning algorithm) directly based on p (y|x) "That is, the classification result y under given feature X" modelThe algorithm we used before (such as logistic regression) is the discriminant learning algorithmPART1 Generation

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 classifi

The general process of naive Bayes 1, Collect data: can use any data. This article uses RSS feeds 2. Prepare data: Numeric or Boolean data required 3, the analysis of data, there are a large number of features, the drawing feature is not small, at this time using histogram effect better 4. Training algorithm: Calculate the conditional probabilities of different

The core idea of naive Bayesian (Naive Bayesian) algorithm is: calculates the probability that a given sample belongs to each classification, and then selects the highest probability as the guessing result . . Assuming that the sample has 2 characteristics x and y, then the probability of its classification 1 is recorded as P (c1|x,y), its value can not be direct

) Def splits (text, L = 20 ):"Return a list of all possible (first, REM) pairs, Len (first) Return [(Text [: I + 1], text [I + 1:])For I in range (min (LEN (text), L)] Def pwords (words ):"The Naive Bayes Probability of a sequence of words ."Return product (PW (w) for W in words) #### Support functions (p. 224) Def product (Nums ):"Return the product of a sequence of numbers ."Return reduce (operator

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 methods, there is the idea of Bayes statistics everywhere.Naive

the display situationLaplace smoothingConditional probability P (w0|1) p (w1|1) p (w2|1), if one is 0, the last flight is also 0. To reduce this effect, all word occurrences can be initialized to 1, and the denominator is initialized to 2.Open bayes.py, and modify lines 4th and 5th of TrainNB0 () to:P0num = ones (numwords); P1num == 2.0; P2denom = 2.0Another problem is that the next overflow is caused by too many decimal multiplies. One solution is to take a natural logarithm of the product, wi

training samples. For example, y = 1 has M1 and training samples have M, then P (y = 1) = m1/m. However, I still cannot figure out the p (x | Y) computation. Naive Bayes hypothesis: P (x1, x2 ,.., XN | y) = P (X1 | Y )... P (XN | y) (x1, x2 ,..., XN is the component of X, that is, the condition is independent. When I! When J is used, P (XI | y, XJ) = P (XI | Y). If y is specified, the occurrence of Xi is

Algorithm grocer--naive Bayesian classification of classification algorithm (Naive Bayesian classification)0, written in front of the wordsI personally always like the algorithm a kind of things, in my opinion algorithm is the ess

Summary:Naive Bayesian classification is a Bayesian classifier, Bayesian classification algorithm is a statistical classification method, using probability statistical knowledge classification, the classification principle is to use the Bayesian formula based on the prior probability of an object to calculate the posteriori probability (that the object belongs to a certain class of probability), Then select the class that has the maximum posteriori pr

Reference Address: http://www.cnblogs.com/leoo2sk/archive/2010/09/17/naive-bayesian-classifier.htmlMy Data mining algorithm implementation Source address: Https://github.com/linyiqun/DataMiningAlgorithmIntroductionTo introduce naive Bayesian algorithm (Naive

The naïve Bayesian algorithm is consistent with the idea of generating learning algorithms in the previous article. It does not need to be like linear regression algorithms to fit a variety of assumptions, only to calculate the probabilities of the various assumptions, and then choose the highest probability of the category of the hypothetical classification. It also adds a Bayesian hypothesis: The attribute value x is independent of each other when g

1. Bayes theoremConditional probability formula:This formula is very simple to calculate the probability of a occurring in the case where B occurs. But many times, it's easy to know P (a| B), the need to calculate is P (b| A), the Bayes theorem will be used:2. Naive Bayesian classificationThe derivation process of naive