Which term refers to the adjustment of a model's hyperparameters to optimize its performance?

The term "model tuning" specifically refers to the process of adjusting a model's hyperparameters to improve its performance on tasks like classification, regression, or other predictive functions. Hyperparameters are the settings or configurations that dictate how the model learns from the training data, such as the learning rate, batch size, or the number of layers in a neural network.

Through model tuning, practitioners aim to find the optimal combination of hyperparameters that enhances the model's ability to generalize well to unseen data, thereby ensuring better accuracy and effectiveness in its predictions. This process typically involves techniques like grid search or random search, and can also include cross-validation to ensure that the model's performance is robust across different subsets of data.

The other terms provided do not pertain to the adjustment of hyperparameters. "Model validation" refers to the techniques to evaluate the model's performance on separate data. "Natural language processing" encompasses a field of AI focused on the interaction between computers and human language, while "narrow AI" describes AI systems designed for specific tasks rather than general intelligence. Thus, "model tuning" is the most accurate term for the adjustment of hyperparameters to optimize model performance.