Solving Imbalanced Data

Oversampling and Undersampling | Data Series | Episode 16.1

In this article we discuss the different methods we can use to over-sample and under-sample from imbalanced data.

What is imbalanced data?

Imbalanced data refers to datasets where the target variable has an imbalance of class frequencies. What do I mean by this? Take for example a dataset that has two classes. This dataset would be considered imbalanced if the target class frequencies might look something like this:

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Imbalanced data is common in examples like manufacturing defects, rare disease diagnosis or fraud detection.

Impacts of imbalanced data on Machine learning Models

• Models can ignore the minority class when being trained
• Bias towards the majority class
• Can cause an accuracy paradox (Just predict the majority class and get a high accuracy performance)

Resampling

There are two techniques we can use to deal with imbalanced data. These are…