naive bayes algorithm

Naive Bayes is a classification method based on Bayesian theorem and independent hypothesis of feature conditions. Simply put, Naive Bayes classifier assumes that each feature of the sample is irrelevant to other features. For example, if a fruit has the characteristics of red, circle, and about 4 inch in diameter, it

Implementation of naive Bayes classifier (php) this article uses php to implement a naive Bayes classifier, which classifies records of discrete variables with discrete attribute values .? After learning the data in the sample.csvfile, the classification model is used to predict the class indexes of the data in predict

Reprinted by the author: By: 88250 Blog: http:/blog.csdn.net/dl88250 MSN Email QQ: DL88250@gmail.com Author: ZY Blog: http:/blog.csdn.net/zyofprogrammer By Sindy E-mail: sindybanana@gmail.com Part 1 The efficiency problem has been solved last time, and many buckets have been fixed. However, after reading some documents, I found a new theoretical problem. Theoretical Problems Naive Bayes text classificatio

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

I have read the naive Bayes classifier over the past two days. Here I will take a simple note based on my own understanding and sort out my ideas. I. Introduction 1. What is a naive Bayes classifier?Naive Bayes ClassifierIt is

indicate that the word appears several times in the document. This creates a training set.Now the naive Bayes method requires that the left part of the equals sign in this famous formula.And the meaning of the left part of the equal sign is essentially, I got a document with so many words in it, what is the probability that my document is classified as Category 1 under this condition? What is the probabili

Bayesian formulas describe the relationship between conditional probabilities. In machine learning, Bayesian formulas can be applied to classification issues. This article is based on my own learning and uses an example of spam classification to deepen my understanding of the theory. Here we will explainSimplicityThe meaning of this word: 1) Each feature is independent of each other, and its appearance is irrelevant to its appearance sequence; 2) each feature is equally important; The abo

Outlook Temperature Humidity Windy Play Yes No Yes No Yes No Yes No Yes No Sunny 2 3 Hot 2 2 High 3 4 False 6 2 9 5 Overcast 4 0 Mild 4 2 Normal 6 1 Trur 3 3 Rainy 3 2 Cool 3 1 As shown in the above table, we will calculate whether to play when the conditions are sunny, cool,

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

] [,2] setosa 0.246 0.1053856 versicolor 1.326 0.1977527 virginica 2.026 0.2746501It is the conditional probability of the feature petal. Width. In this Bayesian implementation, the feature is numeric data (and there is also a fractional part). Here we assume that the probability density conforms to the Gaussian distribution. For example, for the feature petal. width, the probability of being setosa complies with the Gaussian distribution where the mean is 0.246 and the standard variance is 0.10

distribution characteristics, so that the wrong data distribution estimates. In this case, the real test set on the wrong mess (this phenomenon called fitting). But also can not use too simple model, otherwise when the data distribution is more complex, the model is not enough to depict the data distribution (reflected in the training set the error rate is very high, this phenomenon is less than fit). Over-fitting indicates that the model used is more complex than the real data distribution, an

Part 1 Naive BayesOr the junk e-mail classification problem, which was mentioned in the last lesson, is divided into two kinds of event models:1.1. Multivariable Bernouli Event Model"This is the last lesson.Maintain a long and long long dictionaryFor a sample (x, y), X[i]=0or1 Indicates whether dictionary I have appeared in a sample message, Y=0or1 indicates that the sample is spamIn this model, Xi takes a value of only 0or1, so $x _{i} | y$ is Bernou

; - 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

(errorcount)/Len (testset)returnVocablist, p0v, p1v4.7.2 Analysis Data: Displays the area-related terms#lexical display functions with the most table featuresdefgettopwords (NY, SF):Importoperator Vocablist, p0v, p1v=locablwords (NY, SF) TOPNY= []; TOPSF = []#Create a list for meta-ancestor storage forIinchRange (len (p0v)):ifP0v[i] >-6.0: Topsf.append ((Vocablist[i], p0v[i]))ifP1v[i] >-6.0: Topny.append ((Vocablist[i], p1v[i]) SORTEDSF= Sorted (TOPSF, key =LambdaPAIR:PAIR[1], reverse =True

Take the test tomorrow. You can bring your computer to your computer and write the program first. Save your effort to use a calculator ...... Directly use the Python source code. [Python] # Naive Bayes # Calculate the Prob. of class: clsdef P (data, cls_val, cls_name = "class"): cnt = 0.0 for e in data: if e [cls_name] = cls_val: cnt + = 1 return cnt/len (data) # Calculate the Prob (attr | cls) def PT (data

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

NewLISP bayes algorithm, newlispbayes AlgorithmUnderstanding conditional probability To use bayes, first understand the conditional probability. refer to the previous article to understand the conditional probability.Two-phase algorithm-training and query Let's take a look at the famous

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

Bayesian classification is a statistical classification method, which shows good performance in classification problems. It is obvious that naive Bayes is a Bayesian theorem, and the following is a brief review of Bayesian theorem. Before we take a look at the calculation of conditional probabilities, the so-called "conditional probability" (Conditional probability) refers to the probability that event a ta