Which concept refers to the ability of a model to adapt its learning based on new data inputs over time?

The concept that refers to the ability of a model to adapt its learning based on new data inputs over time is online learning. This approach allows algorithms to continuously update their model parameters as new data comes in, enabling them to improve or adjust their predictions and performance dynamically.

Online learning is particularly advantageous in situations where data is constantly being generated or modified, as it facilitates immediate integration of the most recent information into the model's decision-making process. This contrasts with more static learning methods, where a model is trained on a fixed dataset and does not change as new inputs are received.

Model adaptability broadly signifies a model's capacity to change or improve, but it does not specifically highlight the incremental learning process that online learning entails. Generalization refers to a model's ability to perform well on unseen data based on training, and transfer learning is about applying knowledge gained from one task to improve the performance on a different, but related task. Hence, the choice of online learning accurately captures the essence of ongoing adaptation and continuous learning in response to new data.