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 ...

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.

A multi-class classification problem is one where the goal is to predict the value of a variable where there are three or more discrete possibilities. For example, you might want to predict the ...

For example, you could have small, medium, large, and xlarge, or you might have a rating system based on one to five stars. Each of these levels can be considered a class as well. The objective of ...

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 ...

Multi-label Classification is a classification problem where multiple labels may be assigned to each instance. Zero, one or multiple labels can be associated with an instance(or example). It is more ...

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.