Imbalanced Data

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What is imbalanced data? The definition of imbalanced data is straightforward. A dataset is imbalanced if at least one of the classes constitutes only a very small minority. Imbalanced data prevail in banking, insurance, engineering, and many other fields. It is common in fraud detection that the imbalance is on the order of 100 to 1. ... The issue of class imbalance can result in a serious bias towards the majority class, reducing the classification performance and increasing the number of false negatives. How can we alleviate the issue? The most commonly used techniques are data resampling either under-sampling the majority of the class, or over-sampling the minority class, or a mix of both. | Dataman

Classification algorithms tend to perform poorly when data is skewed towards one class, as is often the case when tackling real-world problems such as fraud detection or medical diagnosis. A range of methods exist for addressing this problem, including re-sampling, one-class learning and cost-sensitive learning. | Natalie Hockham

Under-sampling

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Over-sampling

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Precision-Recall (PR) curves will be more informative than ROC

The Relationship Between Precision-Recall and ROC Curves | Jesse Davis & Mark Goadrich - University of Wisconsin-Madison proposes that Precision-Recall (PR) curves will be more informative than ROC when dealing with highly skewed datasets. The PR curves plot precision vs. recall (FPR). Because Precision is directly influenced by class imbalance so the Precision-recall curves are better to highlight differences between models for highly imbalanced data sets. When you compare different models with imbalanced settings, the area under the Precision-Recall curve will be more sensitive than the area under the ROC curve. Using Under-Sampling Techniques for Extremely Imbalanced Data | Dataman - Towards Data Science

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