naive bayes classification

Turn from: http://blog.163.com/[email protected]/blog/static/1712321772010102802635243/Pondering two days, for the naïve Bayesian principle made very clear, but to do text classification, read a lot of articles know based on naive Bayesian formula, compare the maximum value of the posterior probability to classify, the calculation of the posterior probability is from the prior probability and the class cond

Naive Bayesian classificationBy conditional probability: the probability that event a takes place under the premise that event B has occurred is called the conditional probability of event a under event B. Its basic solution formula is:.To derive Bayes theoremThe basic idea of naive Bayes is: for the given

The decision tree and KNN algorithm are the classification algorithms for the result determination , and the data examples are clearly divided into a certain classification.Bayesian: It is not entirely certain that the data instance should be divided into a class , and lake synthesis can only give the probability that the data instance belongs to a given classification.* Introduce a priori probability and l

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 Bayes

Naive Bayesian NBNative Bayes is a simple and effective classification algorithm, and Bayes ' law is represented by this conditional probability formula:P (a| B) = P (b| A) * p (a)/P (B), where P (a| b) means that, in the case of B, the probability of a is occurring, p (A), P (B) represents the probability that a and B

Today in the library for a day of study, the basic principles of naive Bayesian to understand. So write an article to deepen the impression.Of course you should recommend a blog: acdreamers-naive Bayesian classification one, Bayes theorem Conditional probability:P (C|x⃗) =p (x⃗c) p (X⃗) p (c|\vec{x}) = \frac{p ({\vec{

direct conclusion that P (a| B), P (b| A) is difficult to draw directly, but we are more concerned about P (b| A), Bayesian theorem for us to get through the P (a| B) Get P (b| A) the road:The mathematical principles of naive Bayesian classificationThe formal definition of Naive Bayes classification is as follows:1, s

============================================================================================ "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 algorithm used to file, there is need to message ===========================================

1. Naive Bayes hypothesisTo deal with this situation where the dimensionality is too high, we make a hypothesis that each dimension of x is independent of each other. This is also the naïve Bayes hypothesis.Depending on the conditions of the independent distribution, we can easily write P (d| C), as follows:P (d/c) =∏p (ti/c)D represents the document, TI represen

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 ar

Sesame HTTP: Remembering the pitfalls of scikit-learn Bayesian text classification, scikit-learn Bayes Basic steps: 1. Training material classification: I am referring to the official directory structure: Put the corresponding text in each directory, a txt file, and a corresponding article: like the following: Please note that the proportion of all materials s

=train_model (train_x,train_y,classnum)For I in Range (Classnum):Print (Prior_probability[i]) #输出一下每个标签的总共数量Time3=time.time ()Print ("Train data Cost", Time3-time2, "second")Print ("Start predicting data ...")Predict_y=predict (test_x,test_y,prior_probability,conditional_probability)Time4=time.time ()Print ("Predict data Cost", Time4-time3, "second")Print ("Start calculate accuracy ...")Acc=cal_accuracy (test_y,predict_y)Time5=time.time ()Print ("Accuarcy", ACC)Print ("Calculate Accuarcy cost",