The goal of a multi-class classification problem is to predict a value that can be one of three or more possible discrete values, for example "low," "medium" or "high" for a person's annual income.

Multi-class logistic regression is an extension technique that allows you to predict a class that can be one of three or more possible values. An example of multi-class classification is predicting ...

Often when you start learning about classification problems in Machine Learning, you start with binary classification or where there are only two possible outcomes, such as spam or not spam, fraud or ...

Write code to evaluate the model (the trained network) Write code to save and use the model to make predictions for new, previously unseen data; Each of the six steps is complicated. And the six steps ...

Zero, one or multiple labels can be associated with an instance(or example). It is more general than multi-class. classification where one and only one label assigned to an example. You can think the ...

Multi-class classification: We are categorizing emails into six distinct classes. Multinomial Naïve Bayes supports multi-class classification out of the box, making it a clean fit for this problem.