This article explains the concept of Multi-class Classification as a Machine Learning term and as a concept within the AI & Analytics Engine: Multi-class Classification ar objects classified into one among a predetermined set of classes
Multi-class Classification is a type of Classification task where objects are classified into one among a predetermined set of classes. The number of classes can be two or more.
When there are exactly two classes and one is of particular interest, it is best to handle it as a Binary Classification task rather than a Multi-class Classification task.
Multi-class Classification occurs in many use cases, for example:
Fruit classification and grading (superior-quality class, class I, or class II), are based on properties such as size, shape, color, weight, etc.
Predicting what type of failure a machine is likely to undergo (power failure, overstrain, heat, or random failure), in predictive maintenance.
Categorizing the sentiment of customer product reviews (positive, negative, or neutral).
Machine Learning (ML) models help to automate such classification processes to reduce manual labour, in order to satisfy businesses' requirements for large volumes of data prediction every day.
Multi-Class Classification within the AI & Analytics Engine
During the App creation on the AI & Analytics Engine, users can choose "Predict a variable/column" and "Predict a label from a list of labels" to build ML models for their Multi-class Classification problems.
The Engine automatically recommends this option when the column selected for prediction (i.e., the “target column”) appears to contain data with more than two categories.
💡For further information about Multi-class Classification models, check out: