What does training data refer to?

Training data refers to the dataset used to train a machine learning model. This data is crucial as it serves as the foundation for the learning process, allowing the model to understand patterns, relationships, and structures within the data to make accurate predictions or classifications on new, unseen data.

In the context of machine learning, the training data is usually labeled, meaning that the output corresponding to the input data is known, enabling the model to learn by example. The quality and representativeness of the training data directly impact the model's performance and generalization capabilities.

In contrast, processed output from a machine learning model relates to the results generated after the model has been applied to test or validation data and does not constitute training data. Similarly, final test results of an AI application pertain to the evaluation phase after the model has been operationalized, and raw data collected for analysis may not be refined or structured enough to be directly used for training purposes. Thus, it does not align with the specific definition of training data.