Author: Zen and the Art of Computer Programming
1 Introduction
concept introduction
Bayes' theorem (Bayes' theorem) describes how to use known information to calculate the probability of unknown events when the conditional independence assumption is established. The naive Bayes method also belongs to this type of method. Through Bayes theorem, the target variable is classified according to the feature vector in the sample data set. Naive Bayes methods work well for high-dimensional, nonlinear data. Because of its simplicity and ease of implementation, it is widely used in many practical applications. This article will introduce the probability model and theoretical basis of the Naive Bayesian algorithm, and combine practical cases to illustrate the use and significance of the algorithm.
Why use Bayesian?
Bayes' theorem, first proposed by Carl Jacques Friedrich von Bayes, is one of the basic axioms of statistics. It tells us that given certain conditions, the posterior probability is equal to the integral of the prior probability multiplied by the conditional probability. That is, if the probability of something happening is related to the conditions required for it to occur, then we should use this condition as the basis for estimating the probability of the event occurring. The Naive Bayesian method is also based on Bayesian theorem, and is mainly used to deal with classification tasks, that is, given a set of eigenvalues of the data to be predicted, predicting the label of the corresponding data, such as whether the email is spam or fraudulent, etc.
Because of its universal applicability and intuitiveness, Bayes' theorem is used in many fields, including physics, biology, astronomy, engineering technology, economics, etc. For example, if we want to know the relationship between a patient's symptoms and the cause of the disease, we can use Bayes' theorem to calculate such a probability. For another example, when detecting inappropriate speech on social media, we can use Bayesian theorem to determine whether the content posted by the user violates certain laws and regulations. By analyzing and classifying the results of known conditions, we can improve the performance of the classifier. Finally, Bayes' theorem has many other important applications, such as estimating distributions of random variables, solving maximum likelihood estimation problems, etc. all in all&