Another difference between binary and multi-class classification models is how you measure their performance. For binary classification, you can use metrics such as accuracy, precision, recall, F1 ...

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 ...

For example, if most of the data items are class moderate (say, 900 out of 1,000) and only a few are class conservative (say, 40 out of 1,000) and class liberal (60 out of 1,000), then a model that ...

Multi-class logistic regression is a moderately complex technique for multi-class classification problems. The main alternative is to use a neural network classifier with a single hidden layer. A ...

Multi-class weather classification from single images is a fundamental operation in many outdoor computer vision applications. However, it remains difficult and the limited work is carried out for ...

In this paper, we propose an approach for single-trial, multi-class fNIRS classification using a graph representation and a graph neural network (GNN). Specifically, a class-specific graph was ...