What does the term “training data” refer to?

The term “training data” specifically refers to the information used to train an AI system. This data is essential because it allows the model to learn patterns, make predictions, and improve its ability to perform specific tasks. Training data consists of examples that the AI analyzes to understand relationships and features that are crucial for its functions.

For instance, in a machine learning model for image recognition, the training data would include numerous labeled images. The model learns from these examples, gradually refining its algorithms to recognize and classify new images accurately. This process is what enables the AI to generalize from the training examples and apply that knowledge to new, unseen data effectively.

Other options relate to different aspects of data handling or usage in AI and do not accurately encapsulate the concept of training data. Data collected after an AI is deployed is typically referred to as test or operational data, while information structured for human interpretation is related to data presentation rather than the specific training process. Raw data that has not been processed does not qualify as training data, as training data must be pre-processed and labeled to be useful for teaching an AI.