Cost-Sensitive Learning for Imbalanced Classification

Most machine learning algorithms assume that all misclassification errors made by a model are equal.

This is often not the case for imbalanced classification problems where missing a positive or minority class case is worse than incorrectly classifying an example from the negative or majority class. There are many real-world examples, such as detecting spam email, diagnosing a medical condition, or identifying fraud. In all of these cases, a false negative (missing a case) is worse or more costly than a false positive.

Cost-sensitive learning is a subfield of machine learning that takes the costs of prediction errors (and potentially other costs) into account when training a machine learning model. It is a field of study that is closely related to the field of imbalanced learning that is concerned with classification on datasets with a skewed class distribution. As such, many conceptualizations and techniques developed and used for cost-sensitive learning can be adopted for imbalanced classification problems.

In this tutorial, you will discover a gentle introduction to cost-sensitive learning for imbalanced classification.

After completing this tutorial, you will know:

• Imbalanced classification problems often value false-positive classification errors differently from false negatives.
• Cost-sensitive learning is a subfield of machine learning
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