The 5 most useful Techniques to Handle Imbalanced datasets

Have you ever faced an issue where you have such a small sample for the positive class in your dataset that the model is unable to learn?

In such cases, you get a pretty high accuracy just by predicting the majority class, but you fail to capture the minority class, which is most often the point of creating the model in the first place.

Such datasets are a pretty common occurrence and are called as an imbalanced dataset.

Imbalanced datasets are a special case for classification problem where the class distribution is not uniform among the classes. Typically, they are composed by two classes: The majority (negative) class and the minority (positive) class

Imbalanced datasets can be found for different use cases in various domains:

• Finance: Fraud detection datasets commonly have a fraud rate of ~1–2%

• Ad Serving: Click prediction datasets also don’t have a high clickthrough rate.

• Transportation/Airline: Will Airplane failure occur?

• Medical: Does a patient has cancer?

• Content moderation: Does a po…