What type of learning continuously updates the model as new data arrives?

Online learning is a method of machine learning where the model is continuously updated with new data in real-time. This approach allows the model to adapt quickly to changes, making it particularly useful in dynamic environments where data patterns may shift frequently. As new examples become available, the online learning algorithm can immediately incorporate this information, enhancing the model's performance and accuracy over time.

This characteristic of online learning distinguishes it from other methods such as batch learning, where the model is trained on a fixed dataset at once, and once the training is completed, it does not update itself until the next batch of data is processed. Lazy learning refers to algorithms that delay the processing of data, typically storing it until a query is made, rather than continuously updating the model. Static learning implies no updates occur after the initial training phase, which is contrary to the adaptive nature of online learning.