In machine learning, what does the term "model" refer to?

In the context of machine learning, the term "model" refers specifically to a function that makes predictions or decisions based on input data. A model is built from a training process where it learns the underlying patterns from historical data. Once trained, the model can be applied to new, unseen data to generate predictions or classifications.

This function is typically represented mathematically and can take various forms, such as linear equations, decision trees, or neural networks. Its primary purpose is to generalize from the training dataset to new instances, enabling it to infer outcomes or produce predictions based on the features it receives.

The other options do not accurately capture the essence of a model in machine learning. A physical representation of data tends to refer to visualizations or data storage methods, which aren’t the evolving predictive functions used in machine learning. Additionally, while data storage is important for machine learning, it does not define what a model is. Similarly, an input feature for training is a variable used in the model but does not encompass the concept of the model itself, which is fundamentally focused on predictions rather than individual input dimensions.