What is a neural network comprised of?

A neural network is comprised of layers of interconnected nodes, commonly referred to as neurons. Each neuron processes input data and passes its output to the neurons in the next layer. The structure of a neural network typically includes an input layer, one or more hidden layers, and an output layer. This layered architecture allows for the modeling of complex relationships within the data through interconnected pathways, enabling the neural network to learn from the patterns presented in the training data.

In contrast, single-layer algorithms do not have the capacity for deep learning, as they lack the multiple layers that contribute to the complexity of model training. Binary decision trees represent a different modeling approach, where decisions are made based on a series of binary questions, and they do not utilize a layered network of interconnected nodes. Statistical regression models, while useful for understanding relationships between variables, also do not encompass the architecture or functionality of neural networks, which are designed to learn from data in a more sophisticated and multi-dimensional way.