What defines an ML model?

An ML model is defined as a learned representation of data patterns created by algorithms because it encapsulates the underlying relationships and structures found within the training data. Machine learning models are developed through algorithms that process input data, identify patterns, and make predictions or classifications based on that data.

The essence of an ML model is its ability to generalize from the data it has seen during training, allowing it to make accurate predictions on new, unseen data. This learned representation is built through various techniques including supervised learning, unsupervised learning, and reinforcement learning, among others, which enable the model to adapt based on the information present in the training dataset.

Other choices describe components or processes related to ML but do not capture the concept of a model itself. For instance, storing data is a function associated with databases, while a catalog of data inputs refers to documentation or inventory of datasets rather than the model representation. Lastly, a dataset used for testing outputs pertains to the evaluation phase of a model, not the model's definition. Thus, the focus on learned representations of data patterns distinctly identifies what defines an ML model.