Why Is Imbalanced Classification Difficult?

Imbalanced classification is primarily challenging as a predictive modeling task because of the severely skewed class distribution.

This is the cause for poor performance with traditional machine learning models and evaluation metrics that assume a balanced class distribution.

Nevertheless, there are additional properties of a classification dataset that are not only challenging for predictive modeling but also increase or compound the difficulty when modeling imbalanced datasets.

In this tutorial, you will discover data characteristics that compound the challenge of imbalanced classification.

After completing this tutorial, you will know:

  • Imbalanced classification is specifically hard because of the severely skewed class distribution and the unequal misclassification costs.
  • The difficulty of imbalanced classification is compounded by properties such as dataset size, label noise, and data distribution.
  • How to develop an intuition for the compounding effects on modeling difficulty posed by different dataset properties.

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.