Machine Learning

Naive Bayes

A probabilistic classifier based on Bayes’ theorem and feature independence.

Naive Bayes calculates the probability that an example belongs to each class based on its observed features. It assumes that features are conditionally independent once the class is known.

Although this assumption is often unrealistic, the algorithm remains fast and effective for text classification, spam filtering and simple prediction tasks. It also works well with relatively small training datasets.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

Apply

Begin the first module

Become AI native, it is the real deal today, and if it is not for you, you have lost nothing but learnt a new skill.

Apply