naive bayes classification

Bayesian classifierThe classification principle of Bayesian classifier is based on the prior probability of an object, and the Bayesian formula is used to calculate the posteriori probability, that is, the probability of the object belonging to a certain class, and select the class with the maximum posteriori probability as the class to which the object belongs. At present, there are four kinds of Bayesian classifiers, each of which are:

Bayesian classifierThe classification principle of Bayesian classifier is based on the prior probability of an object, and the Bayesian formula is used to calculate the posteriori probability, that is, the probability of the object belonging to a certain class, and select the class with the maximum posteriori probability as the class to which the object belongs. At present, there are four kinds of Bayesian classifiers, each of which are:

Introduction If your understanding of Naive Bayes is still in its infancy, you only understand the basic principles and assumptions and have not implemented product-level code, this article will help you improve the original Naive Bayes algorithm step by step. In this process, you will see some unreasonable aspects and

Scikit-learn package, 1. Details how to use Naive Bayes algorithm in Python Introduction: This article mainly introduces how to use the naïve Bayesian algorithm in Python knowledge. Has a good reference value. Let's take a look at the little series. 2. Introduction to how to use the naive Bayesian algorithm in Python Introduction: This article explains how to

, 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 categ

theoryWhat is naive Bayesian algorithm?Naive Bayesian classifier is a weak classifier based on Bayes theorem, and all naive Bayes classifiers assume that each characteristic of a sample is irrelevant to other characteristics. For example, if a fruit has a red, round, or roug

require feature vector x to be a continuous real number vector. If x is a discrete value, the naive Bayes classification method can be considered. If you want to classify spam and normal emails. Classified mail is an application of text classification. Assume that the simplest feature description method is used. First

1. Preface:Naive Bayes (naive Bayesian) is a simple multi-class classification algorithm, the premise of which is to assume that each feature is independent of each other . Naive Bayes training is mainly for each characteristic, under the condition of a given label, calculat

characteristics of each other independent　　This is the basic formula for naive Bayes classification, so our model can be built as　　And for the denominator in the right side, the denominator is a class-independent formula, that is, for all CK is the same, and then here we just ask for the maximum probability of the category, so the removal of this item will not a

past results and forecast future trends. Currently, several typical data mining researches include association rules, classification, clustering, prediction, and web mining. Classification mining can extract relevant features from data, establish corresponding models or functions, and classify each object in the data into a specific category. For example, you can detect whether the email is spam, whether t

decision.Tree, but then the decision nodes that cannot improve performance in the Development test set are cut. 2. Force the check in a specific order. They force features to be checked in a specific order, even if the feature may beRelatively independent. For example, when a topic-based document (such as a sports, car, or murder mystery), features such as hasword (footBall), which is very likely to represent a specific tag, regardless of the other feature values. It is determined that the sp

. Therefore, the amount of computing is much smaller than that of traversing the entire dataset. This correlation can be manifested in multiple forms. It can be that the user has commented on the item, or just accessed the URL of this link, but no matter what the related method is, we only regard it as two categories, like and dislike. For example, if the score is 1-10, 1-5 means yes, and 6-10 means no. If it is a URL, access is preferred; otherwise, access is disliked. Why is it considered as

calculated based on (Formula 1). Each item on the Right of (Formula 1) can be calculated, for example, P (gray | bucketa) = 2/4, P (gray | bucketb) = 1/3. The stricter calculation method is as follows: P (gray | bucketb) = P (gray andbucketb)/P (bucketb ), P (Gray and bucketb) = 1/7, P (bucketb) = 3/7 So P (gray | bucketb) = P (Gray and bucketb)/P (bucketb) = (1/7)/(3/7) = 1/3 This is the principle of Naive Bayes