Batch learning vs Online learning (2024)

Batch learning and online learning are two different types of machine learning techniques that are used to train models on data.

Batch learning, also known as offline learning, involves training a model on a fixed dataset, or a batch of data, all at once. The model is trained on the entire dataset, and then used to make predictions on new data. This means that batch learning requires a complete dataset before training can begin, and the model cannot be updated once it has been trained without retraining the entire model. Batch learning is commonly used in situations where the dataset is relatively small and can be processed quickly.

On the other hand, online learning, also known as incremental learning or streaming learning, involves training a model on new data as it arrives, one observation at a time. The model is updated each time a new observation is received, allowing it to adapt to changes in the data over time. Online learning is commonly used in situations where the data is too large to be processed all at once, or where the data is constantly changing, such as in stock market data or social media data.

The key difference between batch learning and online learning is that batch learning requires a fixed dataset, while online learning can adapt to new data as it arrives. Batch learning is typically faster and requires less computational resources than online learning, but may not be as flexible in handling changing or large datasets. Online learning, on the other hand, can be more flexible and adaptable, but may require more resources and be slower to process data. Both techniques have their advantages and disadvantages, and the choice between them depends on the specific problem being solved and the characteristics of the data.