Author: Zen and the Art of Computer Programming
1 Introduction
What is Naive Bayesian Classification?
"Naive Bayes" classifier is a simple and effective probabilistic classification method based on Bayes' theorem. The classifier believes that for a given document (or text), each word or phrase is conditionally independent, and its probability of occurrence depends only on the possibility of the word or phrase appearing alone. Naive Bayesian classifiers are able to accurately classify documents by taking into account the independence of each feature. It has the characteristics of high efficiency, accuracy and stability. In practice, Naive Bayesian classifiers usually perform better than other more complex classifiers such as Support Vector Machines (SVM).
Currently, many datasets can be classified using the Naive Bayes algorithm. These include spam filtering, text classification, image recognition, language models, biomarkers, and stock forecasting.
Why Choose Naive Bayesian Classification Algorithm?
1. Easy to understand and implement
The Naive Bayesian classification algorithm is very easy to understand and easy to implement. Its calculation process is more intuitive, and the calculation speed is fast. Don't worry about overcomplicating formula derivations.
2. Small computational overhead
The naive Bayesian classification algorithm does not require a complicated training process, and only needs very little data to obtain good classification results. Therefore, even processing massive amounts of data will not cause too much pressure on computer resources.
3. Applicable to various fields
Naive Bayesian algorithm can handle various types of feature data, such as text data, image data, speech signals, etc. It can also be used for multi-class classification problems, while also adapting to unbalanced sample distributions.
4. Not affected by sample size
Since the Naive Bayesian algorithm is an unsupervised learning algorithm, no additional labeling of the data is required. The classification result depends only on the distribution of the input data and the naive