Description: Overlapping classes in the context of supervised learning refer to a situation in classification problems where different classes share some common characteristics, which can complicate the classification task. This phenomenon occurs when the boundaries between classes are not clearly defined, potentially leading to confusion in assigning labels to new data. Practically, this means that a machine learning model may struggle to distinguish between classes that exhibit significant similarities in their attributes. Overlapping classes pose a significant challenge in the design of classification algorithms, as they require more sophisticated techniques to improve the model’s accuracy and generalization capability. To address this issue, methods such as regularization, feature selection, and the use of more complex classification algorithms that can capture subtleties in the data can be employed. Identifying and managing overlapping classes is crucial in various applications, such as in medical diagnostics, fraud detection, and image classification, where classification errors can have significant consequences.
