Class imbalance occurs when the number of samples in one class, known as the majority class, far exceeds that of the other class, often referred to as the minority class. This phenomenon can be found across diverse industries, and in the realm of machine learning, it can have a substantial impact on the performance of predictive models.
One of the primary challenges posed by class imbalance is the potential for model bias towards the majority class. During the training process, the model is predominantly exposed to samples from the majority class, which makes it more accustomed to patterns associated with this class. Consequently, the model may struggle to accurately predict instances from the minority class, leading to subpar performance in handling such cases.