Which term describes a machine learning model's ability to apply learned patterns to new, unseen data?

The term that accurately describes a machine learning model's ability to apply learned patterns to new, unseen data is generalization. Generalization refers to the model's capacity to understand and predict outcomes based on data that it has not encountered during its training phase. This ability is crucial for the practical application of machine learning, as it determines how effectively the model can perform in real-world scenarios, where it will likely face data variations that differ from the training dataset.

In contrast, misinformation involves incorrect or misleading information, which is not relevant to the operational characteristics of machine learning models. Inference refers more specifically to the process of using a trained model to make predictions or decisions based on new data, rather than the concept of adaptation to new inputs. Input data simply refers to the data provided to the model for training or prediction purposes, without any inherent connection to its ability to generalize. Overall, generalization is essential for ensuring that machine learning models are robust and flexible enough to handle new situations effectively.