What does the term "batch prediction" refer to in data processing?

The term "batch prediction" refers to predictions generated in an offline mode, where a model processes a large dataset all at once rather than making predictions in real time. This approach allows for efficient handling of extensive data, as it often occurs in a controlled environment where the model can leverage the entire dataset rather than focusing on individual data points as they arrive.

Batch prediction is commonly used when immediate results are not necessary, enabling the processing of substantial volumes of data in a single task. This method can improve resource usage and is beneficial in scenarios where predictions need to be generated for multiple instances simultaneously, thereby enhancing overall system performance.

Choosing options that suggest real-time predictions, small datasets, or qualitative analysis does not align with the definition of batch prediction. These alternatives focus on aspects that either imply immediate response needs or different types of data evaluation that do not fit the primary characteristics of batch processing in prediction tasks.