The Naive Bayes classifier is a simple yet effective probabilistic classifier based on Bayes' theorem with the assumption of independence among features. Despite its simplicity, it often performs well and is widely used in various applications such as text classification, spam filtering, and recommendation systems.
The algorithm of the Naive Bayes classifier can be summarized in the following steps:
1.Data Preparation: Gather a labeled dataset where each instance consists of a set of features and a corresponding class label.
2.Feature Selection: Determine which features to include in the classifier. The Naive Bayes classifier assumes that the features are conditionally independent given the class label.
3.Training: Compute the prior probabilities and conditional probabilities from the training dataset. This involves calculating the probability of each class label and the probability distribution of each feature value for each class label.
4.Classification: Given a new instance with unknown class label, calculate the posterior probability for each class label using Bayes' theorem. The class label with the highest posterior probability is assigned to the instance.
5.Evaluation: Measure the accuracy of the classifier by comparing the predicted class labels with the true class labels from a test dataset.