Working with imbalanced data

In this section, we will explore the challenges posed by imbalanced datasets in machine learning and various methods to effectively address this issue. Imbalanced data refers to datasets where one class (the minority class) is significantly underrepresented compared to another class (the majority class). The class imbalance can lead to biased and suboptimal model performance, as models tend to favor the majority class, making accurate predictions for the minority class challenging. We will delve into the consequences of imbalanced data and several techniques to handle imbalanced datasets for improved model performance.

Understanding imbalanced data

Since models prioritize the majority class, there are serious consequences of imbalanced data on model training and evaluation.

In the context of imbalanced datasets in machine learning, the majority class refers to the class that has a significantly larger number of instances or observations compared...