What does the term "batch processing" mean in machine learning?

Batch processing in machine learning refers to the technique of processing a large volume of data all at once rather than handling data as it comes in real time. This approach is particularly useful for training machine learning models on extensive datasets, as it allows the model to learn from a comprehensive set of information, resulting in more robust and accurate predictions. During batch processing, data is collected over a specified time period and then fed into the algorithm simultaneously, which can lead to more efficient computation and better use of resources.

This method contrasts with real-time processing, where data is handled immediately as it is generated, often requiring faster responses. By processing large datasets concurrently, batch processing leverages the computational power of systems and can optimize the training process for machine learning models, enabling tasks like parameter tuning and optimization over more substantial amounts of historical data.

Other options refer to different processing tactics that are not aligned with the batch approach. For instance, real-time data handling focuses on immediate responses, while sequential processing often implies lower volume and speed. Collectively, these distinctions highlight the integral nature of batch processing in harnessing large datasets effectively for machine learning applications.