naive bayes algorithm

Naive Bayesian classification of sparkmlib classification algorithm　　　　(i) naive Bayesian Classification comprehensionNaive Bayesian method is a classification method based on Bayesian theorem and independent hypothesis of characteristic condition. In simple terms, the naive Bayesian classifier assumes that each charac

"person" in the dictionary appears two times in the text) (2) Next, the integral type counting way is normalized, can avoid the sentence length inconsistency problem.Text 1 = "1/3, 1/3, 0, 1/3, 0"Text 2 = "0, 1/4, 1/4, 2/4, 0" B. Establishment of the IDF vector (representing the word frequency information in the whole bag) (1) The document frequency of the entry."1/2, 2/2, 1/2, 2/2, 0/2" To prevent "0" from appearing in ln expressions, the numerator denominator is added "1" (or treated with Lap

Understanding conditional probabilitiesTo understand conditional probabilities, refer to previous articles to understand conditional probabilitiesTwo-stage algorithm-training and queryingNow look at the famous Bayes algorithm. Bayes is divided into two stages: training and querying. Training refers to the training of s

This paper mainly introduces the knowledge of how to use naive Bayesian algorithm in Python. Has a good reference value. Let's take a look at the little series. Again, here's why the title is "using" instead of "Implementing": First, professionals provide algorithms that are higher than our own algorithms, whether efficient or accurate. Secondly, for those who are not good at maths, it is very painful to s

is regular expressions, which can be easily accomplished with regular expressions.The following functions can be used to implement 1:1 #============================================= 2 # input: 3 # bigstring: document string to be converted 4 # output: 5 # List format of documents to be converted 6 #============================================= 7 def textparse (bigstring): 8 import re 9 Listoftokens = Re.split (R ' \w* ', bigstring) return [Tok.lower () for

Again, here's why the title is "using" instead of "Implementing": First, professionals provide algorithms that are higher than our own algorithms, whether efficient or accurate. Secondly, for those who are not good at maths, it is very painful to study a bunch of formulas in order to realize the algorithm. Again, there is no need to "reinvent the wheel" unless the algorithms provided by others meet their own needs. Below the point, do not understand

samples belonging to each class in the training concentration, easily estimated, on the class condition probability p (x| C) estimates, here I only say naive Bayes classifier method, because naive Bayes assumes that the properties of things are independent of each other,P (x| C)=∏p (XI|CI).2. Text categorization proce

In order to complete his graduation thesis, have to contact this naive Bayesian classification algorithm ... I'm so ashamed (I'm going to graduate and learn this ...) also first knowledge)Haha, but it's never too late to learn.To fully understand this algorithm, you must first go to BaiduOriginally naive

Today we introduce naive Bayesian classification algorithm, talk about the basic principles, and then use text classification practice. A simple example Naive Bayesian algorithm is a typical statistical learning method, the main theoretical basis is a Bayesian formula, Bayesian formula is the basic definition as foll

============================================================================================ "Machine Learning Combat" series blog is Bo master reading " Machine learning Combat This book's notes, including the understanding of the algorithm and the Python code implementation of the algorithmIn addition, bloggers here have the machine to learn the actual combat this book all the algorithm source code and

#=============================================2 #Input:3 #bigstring: Document string to convert4 #Output:5 #list format of documents to be converted6 #=============================================7 defTextparse (bigstring):8 ImportRe9Listoftokens = Re.split (r'\w*', bigstring)Ten return[Tok.lower () forTokinchListoftokensifLen (tok) > 2]Note that because of the possibility of whitespace in the result of the segmentation, a layer of filtering is added to the return.The specific use of re

This paper illustrates the Python implementation method of naive Bayesian algorithm. Share to everyone for your reference. The implementation methods are as follows: Advantages and disadvantages of naive Bayesian algorithm Advantages: It is still valid in the case of less data, can deal with many kinds of problems D

This paper will describe the ins and outs of naive Bayesian algorithm, from mathematical derivation to computational walkthrough to programming combat.The content of this article has been compiled and supplemented by reference to network data, Hangyuan Li "Statistical learning method" and Wu "The Beauty of mathematics".Basic Knowledge Supplement:1. Bayesian theory – The beauty of Wu Mathematicshttp://mindha

is very high.3) Classify the new instances:In order to calculate the classification of a new instance, we need to calculate the posteriori probability of the instance belonging to each class, and finally divide this instance into the class with the most posteriori probabilities.The post-test probabilities are:In this case, it is necessary to use the hypothesis of conditional independence, that is, when the classification is determined, the characteristics of X are independent of each other. Bec

Forest　　In order to prevent overfitting, a random forest is equivalent to several decision trees.Four, KNN nearest neighborSince KNN has to traverse all the remaining points each time it looks for the next closest point to it, the algorithm is expensive.V. Naive BayesTo push the probability that the occurrence of event a occurs under B (where events A and B can be decomposed into multiple events), you can