Another Area Where Naive Bayes Algorithm Is Widely Used Is In MultiClass Classification
Naive Bayes
Applications of Naive Bayes
Naive Bayes algorithm is an eager learning algorithm based on Bayes theorem with the assumption that all features are independent given the class label. Naive Bayes algorithm is well suited to text classification as its test time prediction is quick and as a result it can be deployed on a large dataset for inference.
Naive Bayes is usually used in applications where predictions are desired in real time such as fraud detection on credit card transactions. In these applications, a classifier that is capable of performing almost instantaneously is desired. Naive Bayes is also used extensively in text classification, spam filtering and sentiment analysis. In spam filtering, the words of an email or text message serve as the input features to the model, with each word assumed to be independent of others. Naive Bayes usually produces good results when this independent assumption holds true and coupled with its short inference time is sometimes preferred over more complicated classifiers.
Another area where Naive Bayes algorithm is widely used is in multi-class classification. In this domain, multiple classes or categories can be predicted given input features, with probabilities assigned to the predicted classes.
