Imbalanced data refers to classification problems where one class outnumbers other class by a substantial proportion. Imbalanced classification occurs more frequently in binary classification than in multi-level classification. For example, extreme imbalanced data can be seen in banking or financial data where majority credit card uses are acceptable and very few credit card uses are fraudulent.
With an imbalanced dataset, the information required to make an accurate prediction about the minority class cannot be obtained using an algorithm. So, it is recommended to use balanced classification dataset.
Rathnadevi uses fraudulent transactions for his sample, but medical diagnoses is also a good example: suppose 1 person in 10,000 has a particular disease. You’re 99.99% right if you just say nobody has the disease, but that’s a rather unhelpful model.