Imbalanced Data: Analyze First, Sample Later
Classification is a common task in machine learning, where the goal is to classify or label given data or observations. Specifically, in the field of machine learning, binary classification is the most common type. In a binary classification task, many real-world datasets encounter the challenge of imbalanced class distribution, if not all.
Imbalanced class distribution occurs when a dataset with binary target classes contains a majority of data from one class. Machine learning algorithms require a sufficient amount of data to be well-trained and provide unbiased and generalized predictive outputs. When the dataset is imbalanced, the machine learning model becomes biased toward the majority class. It can learn the underlying pattern of the data from the majority class but struggles to learn from the minority class, ultimately failing to predict the minority class accurately.
In practical applications, the ratio between minority and majority classes can be quite drastic, such as 1 to 100, 1 to 1,000, or even 1 to 10,000. For instance, consider the field of financial fraud detection, where the number of fraudulent transactions is exceedingly small compared to legitimate ones, possibly as rare as 1 fraudulent transaction for every 10,000 legitimate ones. This stark class imbalance poses a formidable challenge for building accurate models.
