Multi-Class Imbalanced Classification

Last Updated on August 21, 2020

Imbalanced classification are those prediction tasks where the distribution of examples across class labels is not equal.

Most imbalanced classification examples focus on binary classification tasks, yet many of the tools and techniques for imbalanced classification also directly support multi-class classification problems.

In this tutorial, you will discover how to use the tools of imbalanced classification with a multi-class dataset.

After completing this tutorial, you will know:

• About the glass identification standard imbalanced multi-class prediction problem.
• How to use SMOTE oversampling for imbalanced multi-class classification.
• How to use cost-sensitive learning for imbalanced multi-class classification.

Kick-start your project with my new book Imbalanced Classification with Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

Tutorial Overview

This tutorial is divided into three parts; they are:

1. Glass Multi-Class Classification Dataset
2. SMOTE Oversampling for Multi-Class Classification
3. Cost-Sensitive Learning for Multi-Class Classification

Glass Multi-Class Classification Dataset

In this tutorial, we will focus on
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