Solving The Class Imbalance Problem

Imbalanced classification is a common problem in machine learning, particularly in the realm of binary classification. This occurs when the training dataset has an unequal distribution of classes, leading to a potential bias in the trained model. Examples of imbalanced classification problems include fraud detection, claim prediction, default prediction, churn prediction, spam detection, anomaly detection, and outlier detection. It is important to address the class imbalance in order to improve the performance of our model and ensure its accuracy. Notice that most, if not all, of the examples, are likely binary classification problems. So, Imbalance is Common! In this post, we will examine three methods for addressing this problem in order to improve the performance and accuracy of our models. We will also discuss the importance of choosing the right metric for these types of tasks.   From multi-class to bi-class We will cover the concept of binary classification and how it can be utilized to address the challenges of class imbalance. Binary classification involves dividing a dataset into two groups: a positive group and a negative group. These principles can also be extended to multi-class problems by decomposing the problem into multiple two-class problems. This technique allows us to address class imbalance and utilize a range of methods to enhance the performance of our model. <a href="https://towardsdatascience.com/solving-the-class-imbalance-problem-58cb926b5a0f">Click Here</a>