In machine learning, what is the purpose of the hidden layers?

The purpose of hidden layers in machine learning is to perform computations that help in learning patterns. Hidden layers are critical components of neural networks, as they allow the model to transform the data and extract meaningful features from the input. Each hidden layer applies a series of weight transformations, activation functions, and nonlinearities, which enables the network to capture complex relationships within the data.

This multilayer structure enhances the model's capability to learn hierarchical representations. For example, in image processing, lower layers might identify edges, while deeper layers could recognize shapes or specific objects. Thus, hidden layers play a fundamental role in interpreting the input data and making it possible for the model to improve its accuracy in predictions or classifications based on the patterns it has learned.

In contrast, the other options do not fully capture the core function of hidden layers within the context of machine learning. Connecting input and output layers is not an active function of hidden layers. While data visualization can be enriched through various techniques, it is not the purpose of hidden layers. Serving as the primary interface pertains more to the input and output processes rather than the core computational tasks hidden layers perform.