Summary of Batch and online learning

By Dr Mabrouka Abuhmida

Batch learning and online learning are two different approaches to machine learning, which refer to teaching a computer to make predictions or take actions based on data.

Batch learning involves training a machine learning model on a fixed dataset and then using that trained model to make predictions on new data. This approach is suitable for situations where the training data is static and does not change over time, and the model’s performance does not need to be updated frequently. Batch learning is also suitable for tasks that can be completed offline, as it does not require the model to process new data in real-time continuously.

On the other hand, online learning involves training a machine learning model on a continuous stream of data and updating the model as new data becomes available. This approach is suitable for tasks that require the model to continuously learn and adapt to changing data, allowing it to update its predictions and actions in real time. Online learning is often used in applications such as web search, recommendation systems, and fraud detection, where the data constantly changes, and the model’s performance needs to be updated frequently.

In terms of scalability, online learning is generally less scalable than batch learning. Batch learning involves training the model on a fixed dataset, which means that the model’s performance is not updated as new data becomes available. As a result, the model’s performance may degrade over time if the data distribution changes significantly. In contrast, online learning allows the model to continuously learn from new data, which can improve its performance over time. This makes online learning more scalable when the data distribution is frequently changing.

In terms of speed, batch learning is generally faster than online learning. Batch learning involves training the model on a fixed dataset, which means the training process can be completed in a single pass. In contrast, online learning involves continuously updating the model as new data becomes available, which can be slower. However, the speed difference between the two approaches may be less significant in cases where the amount of data being processed is small.

In terms of reliability, both batch learning and online learning can be reliable if they are implemented correctly. Batch learning can be reliable if the training data is representative of the data that the model will be used on and if the model is trained to a sufficient level of accuracy. Online learning can be reliable if the model can continuously learn from new data and adapt to changing data distributions and if the model’s performance is monitored and updated as needed.

Overall, the choice between batch learning and online learning depends on the specific requirements of the task at hand. Batch learning is suitable for tasks where the data is static and does not change over time, and the model’s performance does not need to be updated frequently. Online learning is suitable for tasks where the data changes frequently, and the model needs to adapt to these changes in real-time.

In summary, batch learning is suitable for static data and offline tasks, while online learning is suitable for tasks that require real-time adaptation to changing data.