A neuron, also called a node or unit, is a basic building block of a neural network. It receives inputs, processes them using an activation function, and produces outputs.

Nodes represent random variables or events in the domain being modeled. Each node corresponds to a specific quantity or attribute of interest.

Non-probability sampling is the method of selecting individuals based on non-random criteria, and not every individual in the sampling frame has a chance of being included.

Data normalization, also known as variable scaling, is a preprocessing step in machine learning that converts the variables of a dataset to a similar range.