Description: Overlapping features refer to the situation where different machine learning models, neural networks, and natural language processing systems share common information among their features. This can result in redundancies, where multiple models capture the same information, potentially leading to inefficiencies in learning and increased model complexity. In the context of neural networks, overlapping features can arise when layers extract similar patterns from input data, which may hinder the model’s ability to generalize to new data. In supervised learning, the presence of overlapping features can affect model accuracy, as it may cause the model to focus on redundant patterns instead of learning unique and relevant features. In natural language processing, overlapping features may manifest in the representation of words or phrases that have similar meanings, complicating the task of differentiation between contexts. Overall, identifying and managing overlapping features is crucial for optimizing the performance of machine learning models and improving their generalization capabilities.
