Naive Bayes Algorithm Explained in Less Than 5 Minutes
The Naive Bayes algorithm is a popular machine learning algorithm that is used for classification tasks. It is a simple and effective algorithm that can be applied to a wide variety of problems.
The algorithm has been used to solve problems in a variety of fields, including text classification, spam filtering, and medical diagnosis.
What is the Naive Bayes algorithm?
The Naive Bayes algorithm is a machine learning algorithm that is used for classification. The algorithm is named after the Bayes theorem, which is used to calculate the probability of an event occurring, given the prior knowledge of conditions that might be related to the event.
The Naive Bayes algorithm is a simple and effective way to calculate the probability of a class label given a set of features. The algorithm is particularly well suited for text classification, where the features can be the frequency of certain words in a document.
How does the Naive Bayes algorithm work?
The Naive Bayes algorithm makes predictions by using the probabilities of classes and features.
- The algorithm starts with a set of training data, which is used to calculate the probabilities of classes and…
