Learn what distinguishes binary from multi-class classification models, and how to choose the right one for your machine learning problem.

We use a decision tree classifier below to understand how important features are in the dataset. It is seen that best performance is achieved when 20 features are used for the classification.

Binary image classification is a common task in machine learning, where we aim to classify images into one of two classes. For example, we can train a model to distinguish between cats and dogs based ...

The binary classification technique presented in this article uses a single output node with sigmoid () activation and BCELoss () during training. It is possible to view a binary classification ...

Dr. James McCaffrey of Microsoft Research kicks off a series of four articles that present a complete end-to-end production-quality example of binary classification using a PyTorch neural network, ...

When we use binary tree support vector machine (SVM) to work the multi-classification problems out, we always find that the structure of the binary tree has a large chance and it has a great influence ...

Manufacturers are willing to incorporate Machine Learning (ML) algorithms into their systems, especially those considered as Safety-Critical Systems (SCS). ML algorithms that perform binary ...