Naive Bayes Classifier
Naive Bayes ClassifierIt is a conditional probability classifier based on naive Bayes classification.
Features of Naive Bayes Classifier
Naive Bayes is a simple way to build a classifier. The model assigns class labels represented by feature values to problem instances, which are taken from a finite set. It is not a single algorithm for training such a classifier, but a series of algorithms based on the same principle. All Naive Bayes classifiers assume that sample features are uncorrelated with other features.
For some types of probability models, better classification results can be achieved in the sample set of supervised learning, but in practical applications, the maximum likelihood estimation method is used for parameter estimation of the naive Bayes model, that is, the naive Bayes model is still valid without using Bayesian probability or Bayesian model.
The advantage of the Naive Bayes classifier is that only a small amount of training data is needed to estimate the necessary parameters. Due to the assumption of variable independence, only the means of each variable need to be estimated without determining the entire covariance matrix.