Class Imbalance

In this classroom discussion, the teacher introduces the concept of class imbalance, where one class or label has more or fewer examples than another. A student explains that class imbalance can make it challenging for machine learning models to make accurate predictions. The student suggests two methods to deal with class imbalance: undersampling, which involves removing examples from the majority class, and oversampling, which involves creating new examples of the minority class. To make it simpler, the student uses the analogy of making teams with friends, where balancing the number of players on each team can be achieved by either removing a friend from one team (undersampling) or adding a friend to the other team (oversampling). The teacher acknowledges the student’s explanation and expresses satisfaction with the analogy